Fix async client update_project method (#2155)

Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>

.env.example

@@ -1 +0,0 @@
-OPENAI_API_KEY="your-openai-api-key"

.github/workflows/cd.yml

@@ -2,14 +2,13 @@ name: Publish Python 🐍 distributions 📦 to PyPI and TestPyPI
 
 on:
   release:
-    types: [published] # This will trigger the workflow when you create a new release
+    types: [published]
 
 jobs:
   build-n-publish:
     name: Build and publish Python 🐍 distributions 📦 to PyPI and TestPyPI
     runs-on: ubuntu-latest
     permissions:
-      # IMPORTANT: this permission is mandatory for trusted publishing
       id-token: write
     steps:
       - uses: actions/checkout@v2
@@ -25,16 +24,23 @@ jobs:
           echo "$HOME/.local/bin" >> $GITHUB_PATH
 
       - name: Install dependencies
-        run: poetry install
+        run: |
+          cd embedchain
+          poetry install
 
       - name: Build a binary wheel and a source tarball
-        run: poetry build
+        run: |
+          cd embedchain
+          poetry build
 
       - name: Publish distribution 📦 to Test PyPI
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
           repository_url: https://test.pypi.org/legacy/
+          packages_dir: embedchain/dist/
 
       - name: Publish distribution 📦 to PyPI
         if: startsWith(github.ref, 'refs/tags')
-        uses: pypa/gh-action-pypi-publish@release/v1
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          packages_dir: embedchain/dist/

.github/workflows/ci.yml

@@ -4,22 +4,40 @@ on:
   push:
     branches: [main]
     paths:
-      - 'embedchain/**'
+      - 'mem0/**'
       - 'tests/**'
-      - 'examples/**'
+      - 'embedchain/**'
   pull_request:
     paths:
-      - 'embedchain/**'
+      - 'mem0/**'
       - 'tests/**'
-      - 'examples/**'
+      - 'embedchain/**'
 
 jobs:
-  build:
+  check_changes:
+    runs-on: ubuntu-latest
+    outputs:
+      mem0_changed: ${{ steps.filter.outputs.mem0 }}
+      embedchain_changed: ${{ steps.filter.outputs.embedchain }}
+    steps:
+    - uses: actions/checkout@v3
+    - uses: dorny/paths-filter@v2
+      id: filter
+      with:
+        filters: |
+          mem0:
+            - 'mem0/**'
+            - 'tests/**'
+          embedchain:
+            - 'embedchain/**'
+
+  build_mem0:
+    needs: check_changes
+    if: needs.check_changes.outputs.mem0_changed == 'true'
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
-
+        python-version: ["3.10", "3.11"]
     steps:
       - uses: actions/checkout@v3
       - name: Set up Python ${{ matrix.python-version }}
@@ -37,17 +55,48 @@ jobs:
         uses: actions/cache@v2
         with:
           path: .venv
-          key: venv-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }}
+          key: venv-mem0-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }}
       - name: Install dependencies
         run: make install_all
         if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
-      - name: Lint with ruff
-        run: make lint
       - name: Run tests and generate coverage report
-        run: make coverage
+        run: make test
+
+  build_embedchain:
+    needs: check_changes
+    if: needs.check_changes.outputs.embedchain_changed == 'true'
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.9", "3.10", "3.11"]
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install poetry
+        uses: snok/install-poetry@v1
+        with:
+          version: 1.4.2
+          virtualenvs-create: true
+          virtualenvs-in-project: true
+      - name: Load cached venv
+        id: cached-poetry-dependencies
+        uses: actions/cache@v2
+        with:
+          path: .venv
+          key: venv-embedchain-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }}
+      - name: Install dependencies
+        run: cd embedchain && make install_all
+        if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
+      - name: Lint with ruff
+        run: cd embedchain && make lint
+      - name: Run tests and generate coverage report
+        run: cd embedchain && make coverage
       - name: Upload coverage reports to Codecov
         uses: codecov/codecov-action@v3
         with:
           file: coverage.xml
         env:
-          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}

.gitignore

@@ -103,7 +103,7 @@ ipython_config.py
 # pdm
 #   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
 #pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended not to include it
 #   in version control.
 #   https://pdm.fming.dev/#use-with-ide
 .pdm.toml
@@ -165,7 +165,7 @@ cython_debug/
 # Database
 db
 test-db
-!embedchain/core/db/
+!embedchain/embedchain/core/db/
 
 .vscode
 .idea/
@@ -179,3 +179,9 @@ notebooks/*.yaml
 
 # cache db
 *.db
+
+# local directories for testing
+eval/
+qdrant_storage/
+.crossnote
+testing.ipynb

.pre-commit-config.yaml

@@ -1,20 +1,16 @@
 repos:
-  - repo: https://github.com/psf/black
-    rev: 23.3.0
-    hooks:
-      - id: black
-  - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: 'v0.0.220'
-    hooks:
-      - id: ruff
-        name: ruff
-        # Respect `exclude` and `extend-exclude` settings.
-        args: ["--force-exclude"]
   - repo: local
     hooks:
-      - id: pytest-check
-        name: pytest-check
-        entry: poetry run pytest
+      - id: ruff
+        name: Ruff
+        entry: ruff check
         language: system
-        pass_filenames: false
-        always_run: true
+        types: [python]
+        args: [--fix] 
+
+      - id: isort
+        name: isort
+        entry: isort
+        language: system
+        types: [python]
+        args: ["--profile", "black"]

CONTRIBUTING.md

@@ -1,4 +1,4 @@
-# Contributing to embedchain
+# Contributing to mem0
 
 Let us make contribution easy, collaborative and fun.
 
@@ -10,9 +10,8 @@ To make a contribution, follow these steps:
 2. Do the changes on your fork with dedicated feature branch `feature/f1`
 3. If you modified the code (new feature or bug-fix), please add tests for it
 4. Include proper documentation / docstring and examples to run the feature
-5. Check the linting
-6. Ensure that all tests pass
-7. Submit a pull request
+5. Ensure that all tests pass
+6. Submit a pull request
 
 For more details about pull requests, please read [GitHub's guides](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request).
 
@@ -24,9 +23,7 @@ We use `poetry` as our package manager. You can install poetry by following the
 Please DO NOT use pip or conda to install the dependencies. Instead, use poetry:
 
 ```bash
-poetry install --all-extras
-or
-poetry install --with dev
+make install_all
 
 #activate
 
@@ -41,34 +38,18 @@ To ensure our standards, make sure to install pre-commit before starting to cont
 pre-commit install
 ```
 
-### 🧹 Linting
-
-We use `ruff` to lint our code. You can run the linter by running the following command:
-
-```bash
-make lint
-```
-
-Make sure that the linter does not report any errors or warnings before submitting a pull request.
-
-### Code Formatting with `black`
-
-We use `black` to reformat the code by running the following command:
-
-```bash
-make format
-```
-
 ### 🧪 Testing
 
 We use `pytest` to test our code. You can run the tests by running the following command:
 
 ```bash
-poetry run pytest
+poetry run pytest tests
+
+# or
+
+make test
 ```
 
-Make sure that all tests pass before submitting a pull request.
+Several packages have been removed from Poetry to make the package lighter. Therefore, it is recommended to run `make install_all` to install the remaining packages and ensure all tests pass. Make sure that all tests pass before submitting a pull request.
 
-## 🚀 Release Process
-
-At the moment, the release process is manual. We try to make frequent releases. Usually, we release a new version when we have a new feature or bugfix. A developer with admin rights to the repository will create a new release on GitHub, and then publish the new version to PyPI.
+We look forward to your pull requests and can't wait to see your contributions!

Makefile

@@ -1,42 +1,34 @@
-# Variables
-PYTHON := python3
-PIP := $(PYTHON) -m pip
-PROJECT_NAME := embedchain
+.PHONY: format sort lint
 
-# Targets
-.PHONY: install format lint clean test ci_lint ci_test coverage
+# Variables
+ISORT_OPTIONS = --profile black
+PROJECT_NAME := mem0ai
+
+# Default target
+all: format sort lint
 
 install:
 	poetry install
 
 install_all:
-	poetry install --all-extras
-	poetry run pip install pinecone-text pinecone-client
-
-install_es:
-	poetry install --extras elasticsearch
-
-install_opensearch:
-	poetry install --extras opensearch
-
-install_milvus:
-	poetry install --extras milvus
-
-shell:
-	poetry shell
-
-py_shell:
-	poetry run python
+	poetry install
+	poetry run pip install groq together boto3 litellm ollama chromadb sentence_transformers vertexai \
+	                        google-generativeai elasticsearch
 
+# Format code with ruff
 format:
-	$(PYTHON) -m black .
-	$(PYTHON) -m isort .
+	poetry run ruff format mem0/
 
-clean:
-	rm -rf dist build *.egg-info
+# Sort imports with isort
+sort:
+	poetry run isort mem0/
 
+# Lint code with ruff
 lint:
-	poetry run ruff .
+	poetry run ruff check mem0/
+
+docs:
+	cd docs && mintlify dev
 
 build:
 	poetry build
@@ -44,9 +36,8 @@ build:
 publish:
 	poetry publish
 
-# for example: make test file=tests/test_factory.py
-test:
-	poetry run pytest $(file)
+clean:
+	poetry run rm -rf dist
 
-coverage:
-	poetry run pytest --cov=$(PROJECT_NAME) --cov-report=xml
+test:
+	poetry run pytest tests

README.md

@@ -1,129 +1,228 @@
 <p align="center">
-  <img src="docs/logo/dark.svg" width="400px" alt="Embedchain Logo">
-</p>
-
-<p align="center">
-  <a href="https://runacap.com/ross-index/q3-2023/" target="_blank" rel="noopener"><img style="width: 260px; height: 56px" src="https://runacap.com/wp-content/uploads/2023/10/ROSS_badge_black_Q3_2023.svg" alt="ROSS Index - Fastest Growing Open-Source Startups in Q3 2023 | Runa Capital" width="260" height="56"/></a>
-</p>
-
-<p align="center">
-  <a href="https://pypi.org/project/embedchain/">
-    <img src="https://img.shields.io/pypi/v/embedchain" alt="PyPI">
+  <a href="https://github.com/mem0ai/mem0">
+  <img src="docs/images/banner-sm.png" width="800px" alt="Mem0 - The Memory Layer for Personalized AI">
   </a>
-  <a href="https://pepy.tech/project/embedchain">
-    <img src="https://static.pepy.tech/badge/embedchain" alt="Downloads">
+<p align="center" style="display: flex; justify-content: center; gap: 20px; align-items: center;">
+  <a href="https://trendshift.io/repositories/11194" target="_blank">
+    <img src="https://trendshift.io/api/badge/repositories/11194" alt="mem0ai%2Fmem0 | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/>
   </a>
-  <a href="https://embedchain.ai/slack">
-    <img src="https://img.shields.io/badge/slack-embedchain-brightgreen.svg?logo=slack" alt="Slack">
-  </a>
-  <a href="https://embedchain.ai/discord">
-    <img src="https://dcbadge.vercel.app/api/server/6PzXDgEjG5?style=flat" alt="Discord">
-  </a>
-  <a href="https://twitter.com/embedchain">
-    <img src="https://img.shields.io/twitter/follow/embedchain" alt="Twitter">
-  </a>
-  <a href="https://colab.research.google.com/drive/138lMWhENGeEu7Q1-6lNbNTHGLZXBBz_B?usp=sharing">
-    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab">
-  </a>
-  <a href="https://codecov.io/gh/embedchain/embedchain">
-    <img src="https://codecov.io/gh/embedchain/embedchain/graph/badge.svg?token=EMRRHZXW1Q" alt="codecov">
+  <a href="https://www.ycombinator.com/launches/LpA-mem0-open-source-memory-layer-for-ai-apps" target="_blank">
+    <img alt="Launch YC: Mem0 - Open Source Memory Layer for AI Apps" src="https://www.ycombinator.com/launches/LpA-mem0-open-source-memory-layer-for-ai-apps/upvote_embed.svg"/>
   </a>
 </p>
 
-<hr />
 
-## What is Embedchain?
+  <p align="center">
+    <a href="https://mem0.ai">Learn more</a>
+    ·
+    <a href="https://mem0.dev/DiG">Join Discord</a>
+  </p>
+</p>
 
-Embedchain is an Open Source RAG Framework that makes it easy to create and deploy AI apps. At its core, Embedchain follows the design principle of being *"Conventional but Configurable"* to serve both software engineers and machine learning engineers.
+<p align="center">
+  <a href="https://mem0.dev/DiG">
+    <img src="https://dcbadge.vercel.app/api/server/6PzXDgEjG5?style=flat" alt="Mem0 Discord">
+  </a>
+  <a href="https://pepy.tech/project/mem0ai">
+    <img src="https://img.shields.io/pypi/dm/mem0ai" alt="Mem0 PyPI - Downloads" >
+  </a>
+  <a href="https://github.com/mem0ai/mem0">
+    <img src="https://img.shields.io/github/commit-activity/m/mem0ai/mem0?style=flat-square" alt="GitHub commit activity">
+  </a>
+  <a href="https://pypi.org/project/mem0ai" target="_blank">
+        <img src="https://img.shields.io/pypi/v/mem0ai?color=%2334D058&label=pypi%20package" alt="Package version">
+    </a>
+    <a href="https://www.npmjs.com/package/mem0ai" target="_blank">
+        <img src="https://img.shields.io/npm/v/mem0ai" alt="Npm package">
+    </a>
+  <a href="https://www.ycombinator.com/companies/mem0">
+    <img src="https://img.shields.io/badge/Y%20Combinator-S24-orange?style=flat-square" alt="Y Combinator S24">
+  </a>
+</p>
 
-Embedchain streamlines the creation of Retrieval-Augmented Generation (RAG) applications, offering a seamless process for managing various types of unstructured data. It efficiently segments data into manageable chunks, generates relevant embeddings, and stores them in a vector database for optimized retrieval. With a suite of diverse APIs, it enables users to extract contextual information, find precise answers, or engage in interactive chat conversations, all tailored to their own data.
 
-## 🔧 Quick install
+# Introduction
 
-### Python API
+[Mem0](https://mem0.ai) (pronounced as "mem-zero") enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions. Mem0 remembers user preferences, adapts to individual needs, and continuously improves over time, making it ideal for customer support chatbots, AI assistants, and autonomous systems.
+
+<!-- Start of Selection -->
+<p style="display: flex;">
+  <span style="font-size: 1.2em;">New Feature: Introducing Graph Memory. Check out our <a href="https://docs.mem0.ai/open-source/graph-memory" target="_blank">documentation</a>.</span>
+</p>
+<!-- End of Selection -->
+
+
+### Core Features
+
+- **Multi-Level Memory**: User, Session, and AI Agent memory retention
+- **Adaptive Personalization**: Continuous improvement based on interactions
+- **Developer-Friendly API**: Simple integration into various applications
+- **Cross-Platform Consistency**: Uniform behavior across devices
+- **Managed Service**: Hassle-free hosted solution
+
+### How Mem0 works?
+
+Mem0 leverages a hybrid database approach to manage and retrieve long-term memories for AI agents and assistants. Each memory is associated with a unique identifier, such as a user ID or agent ID, allowing Mem0 to organize and access memories specific to an individual or context.
+
+When a message is added to the Mem0 using add()  method, the system extracts relevant facts and preferences and stores it across data stores: a vector database, a key-value database, and a graph database. This hybrid approach ensures that different types of information are stored in the most efficient manner, making subsequent searches quick and effective.
+
+When an AI agent or LLM needs to recall memories, it uses the search() method. Mem0 then performs search across these data stores, retrieving relevant information from each source. This information is then passed through a scoring layer, which evaluates their importance based on relevance, importance, and recency. This ensures that only the most personalized and useful context is surfaced.
+
+The retrieved memories can then be appended to the LLM's prompt as needed, enhancing the personalization and relevance of its responses.
+
+### Use Cases
+
+Mem0 empowers organizations and individuals to enhance:
+
+- **AI Assistants and agents**: Seamless conversations with a touch of déjà vu
+- **Personalized Learning**: Tailored content recommendations and progress tracking
+- **Customer Support**: Context-aware assistance with user preference memory
+- **Healthcare**: Patient history and treatment plan management
+- **Virtual Companions**: Deeper user relationships through conversation memory
+- **Productivity**: Streamlined workflows based on user habits and task history
+- **Gaming**: Adaptive environments reflecting player choices and progress
+
+## Get Started
+
+The easiest way to set up Mem0 is through the managed [Mem0 Platform](https://app.mem0.ai). This hosted solution offers automatic updates, advanced analytics, and dedicated support. [Sign up](https://app.mem0.ai) to get started.
+
+If you prefer to self-host, use the open-source Mem0 package. Follow the [installation instructions](#install) to get started.
+
+## Installation Instructions <a name="install"></a>
+
+Install the Mem0 package via pip:
 
 ```bash
-pip install embedchain
+pip install mem0ai
 ```
 
-## ✨ Live demo
+Alternatively, you can use Mem0 with one click on the hosted platform [here](https://app.mem0.ai/).
 
-Checkout the [Chat with PDF](https://embedchain.ai/demo/chat-pdf) live demo we created using Embedchain. You can find the source code [here](https://github.com/embedchain/embedchain/tree/main/examples/chat-pdf).
+### Basic Usage
 
-## 🔍 Usage
+Mem0 requires an LLM to function, with `gpt-4o` from OpenAI as the default. However, it supports a variety of LLMs; for details, refer to our [Supported LLMs documentation](https://docs.mem0.ai/llms).
 
-<!-- Demo GIF or Image -->
-<p align="center">
-  <img src="docs/images/cover.gif" width="900px" alt="Embedchain Demo">
-</p>
+First step is to instantiate the memory:
 
-For example, you can create an Elon Musk bot using the following code:
+```python
+from mem0 import Memory
+
+m = Memory()
+```
+
+<details>
+<summary>How to set OPENAI_API_KEY</summary>
 
 ```python
 import os
-from embedchain import App
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+```
+</details>
 
-# Create a bot instance
-os.environ["OPENAI_API_KEY"] = "YOUR API KEY"
-elon_bot = App()
 
-# Embed online resources
-elon_bot.add("https://en.wikipedia.org/wiki/Elon_Musk")
-elon_bot.add("https://www.forbes.com/profile/elon-musk")
+You can perform the following task on the memory:
 
-# Query the bot
-elon_bot.query("How many companies does Elon Musk run and name those?")
-# Answer: Elon Musk currently runs several companies. As of my knowledge, he is the CEO and lead designer of SpaceX, the CEO and product architect of Tesla, Inc., the CEO and founder of Neuralink, and the CEO and founder of The Boring Company. However, please note that this information may change over time, so it's always good to verify the latest updates.
+1. Add: Store a memory from any unstructured text
+2. Update: Update memory of a given memory_id
+3. Search: Fetch memories based on a query
+4. Get: Return memories for a certain user/agent/session
+5. History: Describe how a memory has changed over time for a specific memory ID
+
+```python
+# 1. Add: Store a memory from any unstructured text
+result = m.add("I am working on improving my tennis skills. Suggest some online courses.", user_id="alice", metadata={"category": "hobbies"})
+
+# Created memory --> 'Improving her tennis skills.' and 'Looking for online suggestions.'
 ```
 
-You can also try it in your browser with Google Colab:
+```python
+# 2. Update: update the memory
+result = m.update(memory_id=<memory_id_1>, data="Likes to play tennis on weekends")
 
-[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/17ON1LPonnXAtLaZEebnOktstB_1cJJmh?usp=sharing)
+# Updated memory --> 'Likes to play tennis on weekends.' and 'Looking for online suggestions.'
+```
 
-## 📖 Documentation
-Comprehensive guides and API documentation are available to help you get the most out of Embedchain:
+```python
+# 3. Search: search related memories
+related_memories = m.search(query="What are Alice's hobbies?", user_id="alice")
 
-- [Introduction](https://docs.embedchain.ai/get-started/introduction#what-is-embedchain)
-- [Getting Started](https://docs.embedchain.ai/get-started/quickstart)
-- [Examples](https://docs.embedchain.ai/examples)
-- [Supported data types](https://docs.embedchain.ai/components/data-sources/overview)
+# Retrieved memory --> 'Likes to play tennis on weekends'
+```
 
-## 🔗 Join the Community
+```python
+# 4. Get all memories
+all_memories = m.get_all()
+memory_id = all_memories["memories"][0] ["id"] # get a memory_id
 
-* Connect with fellow developers by joining our [Slack Community](https://embedchain.ai/slack) or [Discord Community](https://embedchain.ai/discord).
+# All memory items --> 'Likes to play tennis on weekends.' and 'Looking for online suggestions.'
+```
 
-* Dive into [GitHub Discussions](https://github.com/embedchain/embedchain/discussions), ask questions, or share your experiences.
+```python
+# 5. Get memory history for a particular memory_id
+history = m.history(memory_id=<memory_id_1>)
 
-## 🤝 Schedule a 1-on-1 Session
+# Logs corresponding to memory_id_1 --> {'prev_value': 'Working on improving tennis skills and interested in online courses for tennis.', 'new_value': 'Likes to play tennis on weekends' }
+```
 
-Book a [1-on-1 Session](https://cal.com/taranjeetio/ec) with the founders, to discuss any issues, provide feedback, or explore how we can improve Embedchain for you.
+> [!TIP]
+> If you prefer a hosted version without the need to set up infrastructure yourself, check out the [Mem0 Platform](https://app.mem0.ai/) to get started in minutes.
 
-## 🌐 Contributing
 
-Contributions are welcome! Please check out the issues on the repository, and feel free to open a pull request.
-For more information, please see the [contributing guidelines](CONTRIBUTING.md).
+### Graph Memory
+To initialize Graph Memory you'll need to set up your configuration with graph store providers.
+Currently, we support Neo4j as a graph store provider. You can setup [Neo4j](https://neo4j.com/) locally or use the hosted [Neo4j AuraDB](https://neo4j.com/product/auradb/). 
+Moreover, you also need to set the version to `v1.1` (*prior versions are not supported*). 
+Here's how you can do it:
 
-For more reference, please go through [Development Guide](https://docs.embedchain.ai/contribution/dev) and [Documentation Guide](https://docs.embedchain.ai/contribution/docs).
+```python
+from mem0 import Memory
 
-<a href="https://github.com/embedchain/embedchain/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=embedchain/embedchain" />
+config = {
+    "graph_store": {
+        "provider": "neo4j",
+        "config": {
+            "url": "neo4j+s://xxx",
+            "username": "neo4j",
+            "password": "xxx"
+        }
+    },
+    "version": "v1.1"
+}
+
+m = Memory.from_config(config_dict=config)
+
+```
+
+## Documentation
+
+For detailed usage instructions and API reference, visit our documentation at [docs.mem0.ai](https://docs.mem0.ai). Here, you can find more information on both the open-source version and the hosted [Mem0 Platform](https://app.mem0.ai).
+
+## Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=mem0ai/mem0&type=Date)](https://star-history.com/#mem0ai/mem0&Date)
+
+## Support
+
+Join our community for support and discussions. If you have any questions, feel free to reach out to us using one of the following methods:
+
+- [Join our Discord](https://mem0.dev/DiG)
+- [Follow us on Twitter](https://x.com/mem0ai)
+- [Email founders](mailto:founders@mem0.ai)
+
+## Contributors
+
+Join our [Discord community](https://mem0.dev/DiG) to learn about memory management for AI agents and LLMs, and connect with Mem0 users and contributors. Share your ideas, questions, or feedback in our [GitHub Issues](https://github.com/mem0ai/mem0/issues).
+
+We value and appreciate the contributions of our community. Special thanks to our contributors for helping us improve Mem0.
+
+<a href="https://github.com/mem0ai/mem0/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=mem0ai/mem0" />
 </a>
 
 ## Anonymous Telemetry
 
-We collect anonymous usage metrics to enhance our package's quality and user experience. This includes data like feature usage frequency and system info, but never personal details. The data helps us prioritize improvements and ensure compatibility. If you wish to opt-out, set the environment variable `EC_TELEMETRY=false`. We prioritize data security and don't share this data externally.
+We collect anonymous usage metrics to enhance our package's quality and user experience. This includes data like feature usage frequency and system info, but never personal details. The data helps us prioritize improvements and ensure compatibility. If you wish to opt-out, set the environment variable MEM0_TELEMETRY=false. We prioritize data security and don't share this data externally.
 
-## Citation
+## License
 
-If you utilize this repository, please consider citing it with:
-
-```
-@misc{embedchain,
-  author = {Taranjeet Singh, Deshraj Yadav},
-  title = {Embedchain: The Open Source RAG Framework},
-  year = {2023},
-  publisher = {GitHub},
-  journal = {GitHub repository},
-  howpublished = {\url{https://github.com/embedchain/embedchain}},
-}
-```
+This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details.

configs/ollama.yaml

@@ -1,12 +0,0 @@
-llm:
-  provider: ollama
-  config:
-    model: 'llama2'
-    temperature: 0.5
-    top_p: 1
-    stream: true
-
-embedder:
-  provider: huggingface
-  config:
-    model: 'BAAI/bge-small-en-v1.5'

cookbooks/customer-support-chatbot.ipynb

@@ -0,0 +1,239 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from typing import List, Dict\n",
+    "from mem0 import Memory\n",
+    "from datetime import datetime\n",
+    "import anthropic\n",
+    "\n",
+    "# Set up environment variables\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"your_openai_api_key\" # needed for embedding model\n",
+    "os.environ[\"ANTHROPIC_API_KEY\"] = \"your_anthropic_api_key\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SupportChatbot:\n",
+    "    def __init__(self):\n",
+    "        # Initialize Mem0 with Anthropic's Claude\n",
+    "        self.config = {\n",
+    "            \"llm\": {\n",
+    "                \"provider\": \"anthropic\",\n",
+    "                \"config\": {\n",
+    "                    \"model\": \"claude-3-5-sonnet-latest\",\n",
+    "                    \"temperature\": 0.1,\n",
+    "                    \"max_tokens\": 2000,\n",
+    "                }\n",
+    "            }\n",
+    "        }\n",
+    "        self.client = anthropic.Client(api_key=os.environ[\"ANTHROPIC_API_KEY\"])\n",
+    "        self.memory = Memory.from_config(self.config)\n",
+    "\n",
+    "        # Define support context\n",
+    "        self.system_context = \"\"\"\n",
+    "        You are a helpful customer support agent. Use the following guidelines:\n",
+    "        - Be polite and professional\n",
+    "        - Show empathy for customer issues\n",
+    "        - Reference past interactions when relevant\n",
+    "        - Maintain consistent information across conversations\n",
+    "        - If you're unsure about something, ask for clarification\n",
+    "        - Keep track of open issues and follow-ups\n",
+    "        \"\"\"\n",
+    "\n",
+    "    def store_customer_interaction(self,\n",
+    "                                 user_id: str,\n",
+    "                                 message: str,\n",
+    "                                 response: str,\n",
+    "                                 metadata: Dict = None):\n",
+    "        \"\"\"Store customer interaction in memory.\"\"\"\n",
+    "        if metadata is None:\n",
+    "            metadata = {}\n",
+    "\n",
+    "        # Add timestamp to metadata\n",
+    "        metadata[\"timestamp\"] = datetime.now().isoformat()\n",
+    "\n",
+    "        # Format conversation for storage\n",
+    "        conversation = [\n",
+    "            {\"role\": \"user\", \"content\": message},\n",
+    "            {\"role\": \"assistant\", \"content\": response}\n",
+    "        ]\n",
+    "\n",
+    "        # Store in Mem0\n",
+    "        self.memory.add(\n",
+    "            conversation,\n",
+    "            user_id=user_id,\n",
+    "            metadata=metadata\n",
+    "        )\n",
+    "\n",
+    "    def get_relevant_history(self, user_id: str, query: str) -> List[Dict]:\n",
+    "        \"\"\"Retrieve relevant past interactions.\"\"\"\n",
+    "        return self.memory.search(\n",
+    "            query=query,\n",
+    "            user_id=user_id,\n",
+    "            limit=5  # Adjust based on needs\n",
+    "        )\n",
+    "\n",
+    "    def handle_customer_query(self, user_id: str, query: str) -> str:\n",
+    "        \"\"\"Process customer query with context from past interactions.\"\"\"\n",
+    "\n",
+    "        # Get relevant past interactions\n",
+    "        relevant_history = self.get_relevant_history(user_id, query)\n",
+    "\n",
+    "        # Build context from relevant history\n",
+    "        context = \"Previous relevant interactions:\\n\"\n",
+    "        for memory in relevant_history:\n",
+    "            context += f\"Customer: {memory['memory']}\\n\"\n",
+    "            context += f\"Support: {memory['memory']}\\n\"\n",
+    "            context += \"---\\n\"\n",
+    "\n",
+    "        # Prepare prompt with context and current query\n",
+    "        prompt = f\"\"\"\n",
+    "        {self.system_context}\n",
+    "\n",
+    "        {context}\n",
+    "\n",
+    "        Current customer query: {query}\n",
+    "\n",
+    "        Provide a helpful response that takes into account any relevant past interactions.\n",
+    "        \"\"\"\n",
+    "\n",
+    "        # Generate response using Claude\n",
+    "        response = self.client.messages.create(\n",
+    "            model=\"claude-3-5-sonnet-latest\",\n",
+    "            messages=[{\"role\": \"user\", \"content\": prompt}],\n",
+    "            max_tokens=2000,\n",
+    "            temperature=0.1\n",
+    "        )\n",
+    "\n",
+    "        # Store interaction\n",
+    "        self.store_customer_interaction(\n",
+    "            user_id=user_id,\n",
+    "            message=query,\n",
+    "            response=response,\n",
+    "            metadata={\"type\": \"support_query\"}\n",
+    "        )\n",
+    "\n",
+    "        return response.content[0].text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Welcome to Customer Support! Type 'exit' to end the conversation.\n",
+      "Customer: Hi, I'm having trouble connecting my new smartwatch to the mobile app. It keeps showing a connection error.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/5x/9kmqjfm947g5yh44m7fjk75r0000gn/T/ipykernel_99777/1076713094.py:55: DeprecationWarning: The current get_all API output format is deprecated. To use the latest format, set `api_version='v1.1'`. The current format will be removed in mem0ai 1.1.0 and later versions.\n",
+      "  return self.memory.search(\n",
+      "/var/folders/5x/9kmqjfm947g5yh44m7fjk75r0000gn/T/ipykernel_99777/1076713094.py:47: DeprecationWarning: The current add API output format is deprecated. To use the latest format, set `api_version='v1.1'`. The current format will be removed in mem0ai 1.1.0 and later versions.\n",
+      "  self.memory.add(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Support: Hello! Thank you for reaching out about the connection issue with your smartwatch. I understand how frustrating it can be when a new device won't connect properly. I'll be happy to help you resolve this.\n",
+      "\n",
+      "To better assist you, could you please provide me with:\n",
+      "1. The model of your smartwatch\n",
+      "2. The type of phone you're using (iOS or Android)\n",
+      "3. Whether you've already installed the companion app on your phone\n",
+      "4. If you've tried pairing the devices before\n",
+      "\n",
+      "These details will help me provide you with the most accurate troubleshooting steps. In the meantime, here are some general tips that might help:\n",
+      "- Make sure Bluetooth is enabled on your phone\n",
+      "- Keep your smartwatch and phone within close range (within 3 feet) during pairing\n",
+      "- Ensure both devices have sufficient battery power\n",
+      "- Check if your phone's operating system meets the minimum requirements for the smartwatch\n",
+      "\n",
+      "Please provide the requested information, and I'll guide you through the specific steps to resolve the connection error.\n",
+      "\n",
+      "Is there anything else you'd like to share about the issue? \n",
+      "\n",
+      "\n",
+      "Customer: The connection issue is still happening even after trying the steps you suggested.\n",
+      "Support: I apologize that you're still experiencing connection issues with your smartwatch. I understand how frustrating it must be to have this problem persist even after trying the initial troubleshooting steps. Let's try some additional solutions to resolve this.\n",
+      "\n",
+      "Before we proceed, could you please confirm:\n",
+      "1. Which specific steps you've already attempted?\n",
+      "2. Are you seeing any particular error message?\n",
+      "3. What model of smartwatch and phone are you using?\n",
+      "\n",
+      "This information will help me provide more targeted solutions and avoid suggesting steps you've already tried. In the meantime, here are a few advanced troubleshooting steps we can consider:\n",
+      "\n",
+      "1. Completely resetting the Bluetooth connection\n",
+      "2. Checking for any software updates for both the watch and phone\n",
+      "3. Testing the connection with a different mobile device to isolate the issue\n",
+      "\n",
+      "Would you be able to provide those details so I can better assist you? I'll make sure to document this ongoing issue to help track its resolution. \n",
+      "\n",
+      "\n",
+      "Customer: exit\n",
+      "Thank you for using our support service. Goodbye!\n"
+     ]
+    }
+   ],
+   "source": [
+    "chatbot = SupportChatbot()\n",
+    "user_id = \"customer_bot\"\n",
+    "print(\"Welcome to Customer Support! Type 'exit' to end the conversation.\")\n",
+    "\n",
+    "while True:\n",
+    "    # Get user input\n",
+    "    query = input()\n",
+    "    print(\"Customer:\", query)\n",
+    "    \n",
+    "    # Check if user wants to exit\n",
+    "    if query.lower() == 'exit':\n",
+    "        print(\"Thank you for using our support service. Goodbye!\")\n",
+    "        break\n",
+    "    \n",
+    "    # Handle the query and print the response\n",
+    "    response = chatbot.handle_customer_query(user_id, query)\n",
+    "    print(\"Support:\", response, \"\\n\\n\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

cookbooks/helper/mem0_teachability.py

@@ -0,0 +1,170 @@
+# Copyright (c) 2023 - 2024, Owners of https://github.com/autogen-ai
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+# Portions derived from  https://github.com/microsoft/autogen are under the MIT License.
+# SPDX-License-Identifier: MIT
+# forked from autogen.agentchat.contrib.capabilities.teachability.Teachability
+
+from typing import Dict, Optional, Union
+from autogen.agentchat.assistant_agent import ConversableAgent
+from autogen.agentchat.contrib.capabilities.agent_capability import AgentCapability
+from autogen.agentchat.contrib.text_analyzer_agent import TextAnalyzerAgent
+from termcolor import colored
+from mem0 import Memory
+
+
+class Mem0Teachability(AgentCapability):
+    def __init__(
+        self,
+        verbosity: Optional[int] = 0,
+        reset_db: Optional[bool] = False,
+        recall_threshold: Optional[float] = 1.5,
+        max_num_retrievals: Optional[int] = 10,
+        llm_config: Optional[Union[Dict, bool]] = None,
+        agent_id: Optional[str] = None,
+        memory_client: Optional[Memory] = None,
+    ):
+        self.verbosity = verbosity
+        self.recall_threshold = recall_threshold
+        self.max_num_retrievals = max_num_retrievals
+        self.llm_config = llm_config
+        self.analyzer = None
+        self.teachable_agent = None
+        self.agent_id = agent_id
+        self.memory = memory_client if memory_client else Memory()
+
+        if reset_db:
+            self.memory.reset()
+
+    def add_to_agent(self, agent: ConversableAgent):
+        self.teachable_agent = agent
+        agent.register_hook(hookable_method="process_last_received_message", hook=self.process_last_received_message)
+
+        if self.llm_config is None:
+            self.llm_config = agent.llm_config
+        assert self.llm_config, "Teachability requires a valid llm_config."
+
+        self.analyzer = TextAnalyzerAgent(llm_config=self.llm_config)
+
+        agent.update_system_message(
+            agent.system_message
+            + "\nYou've been given the special ability to remember user teachings from prior conversations."
+        )
+
+    def process_last_received_message(self, text: Union[Dict, str]):
+        expanded_text = text
+        if self.memory.get_all(agent_id=self.agent_id):
+            expanded_text = self._consider_memo_retrieval(text)
+        self._consider_memo_storage(text)
+        return expanded_text
+
+    def _consider_memo_storage(self, comment: Union[Dict, str]):
+        response = self._analyze(
+            comment,
+            "Does any part of the TEXT ask the agent to perform a task or solve a problem? Answer with just one word, yes or no.",
+        )
+
+        if "yes" in response.lower():
+            advice = self._analyze(
+                comment,
+                "Briefly copy any advice from the TEXT that may be useful for a similar but different task in the future. But if no advice is present, just respond with 'none'.",
+            )
+
+            if "none" not in advice.lower():
+                task = self._analyze(
+                    comment,
+                    "Briefly copy just the task from the TEXT, then stop. Don't solve it, and don't include any advice.",
+                )
+
+                general_task = self._analyze(
+                    task,
+                    "Summarize very briefly, in general terms, the type of task described in the TEXT. Leave out details that might not appear in a similar problem.",
+                )
+
+                if self.verbosity >= 1:
+                    print(colored("\nREMEMBER THIS TASK-ADVICE PAIR", "light_yellow"))
+                self.memory.add(
+                    [{"role": "user", "content": f"Task: {general_task}\nAdvice: {advice}"}], agent_id=self.agent_id
+                )
+
+        response = self._analyze(
+            comment,
+            "Does the TEXT contain information that could be committed to memory? Answer with just one word, yes or no.",
+        )
+
+        if "yes" in response.lower():
+            question = self._analyze(
+                comment,
+                "Imagine that the user forgot this information in the TEXT. How would they ask you for this information? Include no other text in your response.",
+            )
+
+            answer = self._analyze(
+                comment, "Copy the information from the TEXT that should be committed to memory. Add no explanation."
+            )
+
+            if self.verbosity >= 1:
+                print(colored("\nREMEMBER THIS QUESTION-ANSWER PAIR", "light_yellow"))
+            self.memory.add(
+                [{"role": "user", "content": f"Question: {question}\nAnswer: {answer}"}], agent_id=self.agent_id
+            )
+
+    def _consider_memo_retrieval(self, comment: Union[Dict, str]):
+        if self.verbosity >= 1:
+            print(colored("\nLOOK FOR RELEVANT MEMOS, AS QUESTION-ANSWER PAIRS", "light_yellow"))
+        memo_list = self._retrieve_relevant_memos(comment)
+
+        response = self._analyze(
+            comment,
+            "Does any part of the TEXT ask the agent to perform a task or solve a problem? Answer with just one word, yes or no.",
+        )
+
+        if "yes" in response.lower():
+            if self.verbosity >= 1:
+                print(colored("\nLOOK FOR RELEVANT MEMOS, AS TASK-ADVICE PAIRS", "light_yellow"))
+            task = self._analyze(
+                comment, "Copy just the task from the TEXT, then stop. Don't solve it, and don't include any advice."
+            )
+
+            general_task = self._analyze(
+                task,
+                "Summarize very briefly, in general terms, the type of task described in the TEXT. Leave out details that might not appear in a similar problem.",
+            )
+
+            memo_list.extend(self._retrieve_relevant_memos(general_task))
+
+        memo_list = list(set(memo_list))
+        return comment + self._concatenate_memo_texts(memo_list)
+
+    def _retrieve_relevant_memos(self, input_text: str) -> list:
+        search_results = self.memory.search(input_text, agent_id=self.agent_id, limit=self.max_num_retrievals)
+        memo_list = [result["memory"] for result in search_results if result["score"] <= self.recall_threshold]
+
+        if self.verbosity >= 1 and not memo_list:
+            print(colored("\nTHE CLOSEST MEMO IS BEYOND THE THRESHOLD:", "light_yellow"))
+            if search_results["results"]:
+                print(search_results["results"][0])
+            print()
+
+        return memo_list
+
+    def _concatenate_memo_texts(self, memo_list: list) -> str:
+        memo_texts = ""
+        if memo_list:
+            info = "\n# Memories that might help\n"
+            for memo in memo_list:
+                info += f"- {memo}\n"
+            if self.verbosity >= 1:
+                print(colored(f"\nMEMOS APPENDED TO LAST MESSAGE...\n{info}\n", "light_yellow"))
+            memo_texts += "\n" + info
+        return memo_texts
+
+    def _analyze(self, text_to_analyze: Union[Dict, str], analysis_instructions: Union[Dict, str]):
+        self.analyzer.reset()
+        self.teachable_agent.send(
+            recipient=self.analyzer, message=text_to_analyze, request_reply=False, silent=(self.verbosity < 2)
+        )
+        self.teachable_agent.send(
+            recipient=self.analyzer, message=analysis_instructions, request_reply=True, silent=(self.verbosity < 2)
+        )
+        return self.teachable_agent.last_message(self.analyzer)["content"]

docs/README.md

@@ -1,7 +1,14 @@
-# Contributing to embedchain docs
+# Mintlify Starter Kit
 
+Click on `Use this template` to copy the Mintlify starter kit. The starter kit contains examples including
 
-### 👩‍💻 Development
+- Guide pages
+- Navigation
+- Customizations
+- API Reference pages
+- Use of popular components
+
+### Development
 
 Install the [Mintlify CLI](https://www.npmjs.com/package/mintlify) to preview the documentation changes locally. To install, use the following command
 
@@ -15,9 +22,9 @@ Run the following command at the root of your documentation (where mint.json is)
 mintlify dev
 ```
 
-### 😎 Publishing Changes
+### Publishing Changes
 
-Changes will be deployed to production automatically after your PR is merged to the main branch.
+Install our Github App to auto propagate changes from your repo to your deployment. Changes will be deployed to production automatically after pushing to the default branch. Find the link to install on your dashboard. 
 
 #### Troubleshooting
 

docs/_snippets/get-help.mdx

@@ -1,11 +1,11 @@
 <CardGroup cols={3}>
-  <Card title="Talk to founders" icon="calendar" href="https://cal.com/taranjeetio/ec">
-  Schedule a call
+  <Card title="Discord" icon="discord" href="https://mem0.dev/DiD" color="#7289DA">
+    Join our community
   </Card>
-  <Card title="Slack" icon="slack" href="https://embedchain.ai/slack" color="#4A154B">
-    Join our slack community
+  <Card title="GitHub" icon="github" href="https://github.com/mem0ai/mem0/discussions/new?category=q-a">
+    Ask questions on GitHub
   </Card>
-  <Card title="Discord" icon="discord" href="https://discord.gg/6PzXDgEjG5" color="#7289DA">
-    Join our discord community
+  <Card title="Support" icon="calendar" href="https://cal.com/taranjeetio/meet">
+  Talk to founders
   </Card>
 </CardGroup>

docs/api-reference/entities/delete-user.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Delete User'
+openapi: delete /v1/entities/{entity_type}/{entity_id}/
+---

docs/api-reference/entities/get-users.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Get Users'
+openapi: get /v1/entities/
+---

docs/api-reference/memory/add-memories.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Add Memories'
+openapi: post /v1/memories/
+---

docs/api-reference/memory/batch-delete.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Batch Delete Memories'
+openapi: delete /v1/batch/
+---

docs/api-reference/memory/batch-update.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Batch Update Memories'
+openapi: put /v1/batch/
+---

docs/api-reference/memory/create-memory-export.mdx

@@ -0,0 +1,6 @@
+---
+title: 'Create Memory Export'
+openapi: post /v1/exports/
+---
+
+Submit a job to create a structured export of memories using a customizable Pydantic schema. This process may take some time to complete, especially if you’re exporting a large number of memories. You can tailor the export by applying various filters (e.g., user_id, agent_id, run_id, or session_id) and by modifying the Pydantic schema to ensure the final data matches your exact needs.

docs/api-reference/memory/delete-memories.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Delete Memories'
+openapi: delete /v1/memories/
+---

docs/api-reference/memory/delete-memory.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Delete Memory'
+openapi: delete /v1/memories/{memory_id}/
+---

docs/api-reference/memory/get-memory-export.mdx

@@ -0,0 +1,6 @@
+---
+title: 'Get Memory Export'
+openapi: get /v1/exports/
+---
+
+Retrieve the latest structured memory export after submitting an export job. You can filter the export by `user_id`, `run_id`, `session_id`, or `app_id` to get the most recent export matching your filters.

docs/api-reference/memory/get-memory.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Get Memory'
+openapi: get /v1/memories/{memory_id}/
+---

docs/api-reference/memory/history-memory.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Memory History'
+openapi: get /v1/memories/{memory_id}/history/
+---

docs/api-reference/memory/update-memory.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Update Memory'
+openapi: put /v1/memories/{memory_id}/
+---

docs/api-reference/memory/v1-get-memories.mdx

@@ -0,0 +1,4 @@
+---
+title: 'V1 Get Memories'
+openapi: get /v1/memories/
+---

docs/api-reference/memory/v1-search-memories.mdx

@@ -0,0 +1,4 @@
+---
+title: 'V1 Search Memories'
+openapi: post /v1/memories/search/
+---

docs/api-reference/memory/v2-get-memories.mdx

@@ -0,0 +1,74 @@
+---
+title: 'V2 Get Memories'
+openapi: post /v2/memories/
+---
+
+
+Mem0 offers two versions of the get memories API: v1 and v2. Here's how they differ:
+
+<Tabs>
+  <Tab title="v1 Get Memories">
+    <CodeGroup>
+    ```python Code
+    memories = m.get_all(user_id="alex")
+    ```
+
+    ```json Output
+    [
+   {
+      "id":"f38b689d-6b24-45b7-bced-17fbb4d8bac7",
+      "memory":"travelling to Paris",
+      "user_id":"alex",
+      "hash":"62bc074f56d1f909f1b4c2b639f56f6a",
+      "metadata":null,
+      "created_at":"2023-02-25T23:57:00.108347-07:00",
+      "updated_at":"2024-07-25T23:57:00.108367-07:00"
+   }
+]
+    ```
+    </CodeGroup>
+  </Tab>
+
+  <Tab title="v2 Get Memories">
+    <CodeGroup>
+    ```python Code
+    memories = m.get_all(
+        filters={
+            "AND": [
+                {
+                    "user_id": "alex"
+                },
+                {
+                    "created_at": {
+                        "gte": "2024-07-01",
+                        "lte": "2024-07-31"
+                    }
+                }
+            ]
+        },
+        version="v2"
+    )
+    ```
+
+    ```json Output
+    [
+   {
+      "id":"f38b689d-6b24-45b7-bced-17fbb4d8bac7",
+      "memory":"Name: Alex. Vegetarian. Allergic to nuts.",
+      "user_id":"alex",
+      "hash":"62bc074f56d1f909f1b4c2b639f56f6a",
+      "metadata":null,
+      "created_at":"2024-07-25T23:57:00.108347-07:00",
+      "updated_at":"2024-07-25T23:57:00.108367-07:00"
+   }
+]
+    ```
+    </CodeGroup>
+  </Tab>
+</Tabs>
+
+Key difference between v1 and v2 get memories:
+
+• **Filters**: v2 allows you to apply filters to narrow down memory retrieval based on specific criteria. This includes support for complex logical operations (AND, OR) and comparison operators (IN, gte, lte, gt, lt, ne, icontains) for advanced filtering capabilities.
+
+The v2 get memories API is more powerful and flexible, allowing for more precise memory retrieval without the need for a search query.

docs/api-reference/memory/v2-search-memories.mdx

@@ -0,0 +1,85 @@
+---
+title: 'V2 Search Memories'
+openapi: post /v2/memories/search/
+---
+
+Mem0 offers two versions of the search API: v1 and v2. Here's how they differ:
+
+<Tabs>
+  <Tab title="v1 Search">
+    <CodeGroup>
+    ```python Code
+    related_memories = m.search(query="What are Alice's hobbies?", user_id="alice")
+    ```
+
+    ```json Output
+    [
+       {
+          "id":"ea925981-272f-40dd-b576-be64e4871429",
+          "memory":"Likes to play cricket and plays cricket on weekends.",
+          "hash":"c8809002-25c1-4c97-a3a2-227ce9c20c53",
+          "metadata":{
+             "category":"hobbies"
+          },
+          "score":0.32116443111457704,
+          "created_at":"2024-07-26T10:29:36.630547-07:00",
+          "updated_at":"None",
+          "user_id":"alice"
+       }
+    ]
+    ```
+    </CodeGroup>
+  </Tab>
+
+  <Tab title="v2 Search">
+    <CodeGroup>
+    ```python Code
+    related_memories = m.vsearch(
+        query="What are Alice's hobbies?",
+        filters={
+        "AND":[
+            {
+                "user_id":"alice"
+            },
+            {
+                "agent_id":{
+                    "in":[
+                    "travelling",
+                    "sports"
+                    ]
+                }
+            }
+        ]
+    },
+    version="v2"
+    )
+    ```
+
+    ```json Output
+    {
+      "memories": [
+        {
+          "id": "ea925981-272f-40dd-b576-be64e4871429",
+          "memory": "Likes to play cricket and plays cricket on weekends.",
+          "hash": "c8809002-25c1-4c97-a3a2-227ce9c20c53",
+          "metadata": {
+            "category": "hobbies"
+          },
+          "score": 0.32116443111457704,
+          "created_at": "2024-07-26T10:29:36.630547-07:00",
+          "updated_at": null,
+          "user_id": "alice",
+          "agent_id": "sports"
+        }
+      ],
+    }
+    ```
+    </CodeGroup>
+  </Tab>
+</Tabs>
+
+Key difference between v1 and v2 search:
+
+• **Filters**: v2 allows you to apply filters to narrow down search results based on specific criteria. This includes support for complex logical operations (AND, OR) and comparison operators (IN, gte, lte, gt, lt, ne, icontains) for advanced filtering capabilities.
+
+The v2 search API is more powerful and flexible, allowing for more precise memory retrieval.

docs/api-reference/organization/add-org-member.mdx

@@ -0,0 +1,9 @@
+---
+title: 'Add Member'
+openapi: post /api/v1/orgs/organizations/{org_id}/members/
+---
+
+The API provides two roles for organization members:
+
+- `READER`: Allows viewing of organization resources.
+- `OWNER`: Grants full administrative access to manage the organization and its resources.

docs/api-reference/organization/create-org.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Create Organization'
+openapi: post /api/v1/orgs/organizations/
+---

docs/api-reference/organization/delete-org-member.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Delete Member'
+openapi: delete /api/v1/orgs/organizations/{org_id}/members/
+---

docs/api-reference/organization/delete-org.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Delete Organization'
+openapi: delete /api/v1/orgs/organizations/{org_id}/
+---

docs/api-reference/organization/get-org-members.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Get Members'
+openapi: get /api/v1/orgs/organizations/{org_id}/members/
+---

docs/api-reference/organization/get-org.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Get Organization'
+openapi: get /api/v1/orgs/organizations/{org_id}/
+---

docs/api-reference/organization/get-orgs.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Get Organizations'
+openapi: get /api/v1/orgs/organizations/
+---

docs/api-reference/organization/update-org-member.mdx

@@ -0,0 +1,9 @@
+---
+title: 'Update Member'
+openapi: put /api/v1/orgs/organizations/{org_id}/members/
+---
+
+The API provides two roles for organization members:
+
+- `READER`: Allows viewing of organization resources.
+- `OWNER`: Grants full administrative access to manage the organization and its resources.

docs/api-reference/overview.mdx

@@ -0,0 +1,69 @@
+# Mem0 API Overview
+
+Mem0 provides a powerful set of APIs that allow you to integrate advanced memory management capabilities into your applications. Our APIs are designed to be intuitive, efficient, and scalable, enabling you to create, retrieve, update, and delete memories across various entities such as users, agents, apps, and runs.
+
+## Key Features
+
+- **Memory Management**: Add, retrieve, update, and delete memories with ease.
+- **Entity-based Operations**: Perform operations on memories associated with specific users, agents, apps, or runs.
+- **Advanced Search**: Utilize our search API to find relevant memories based on various criteria.
+- **History Tracking**: Access the history of memory interactions for comprehensive analysis.
+- **User Management**: Manage user entities and their associated memories.
+
+## API Structure
+
+Our API is organized into several main categories:
+
+1. **Memory APIs**: Core operations for managing individual memories and collections.
+2. **Entities APIs**: Manage different entity types (users, agents, etc.) and their associated memories.
+3. **Search API**: Advanced search functionality to retrieve relevant memories.
+4. **History API**: Track and retrieve the history of memory interactions.
+
+## Authentication
+
+All API requests require authentication using HTTP Basic Auth. Ensure you include your API key in the Authorization header of each request.
+
+## Organizations and projects (optional)
+
+Organizations and projects provide the following capabilities:
+
+- **Multi-org/project Support**: Specify organization and project when initializing the Mem0 client to attribute API usage appropriately
+- **Member Management**: Control access to data through organization and project membership
+- **Access Control**: Only members can access memories and data within their organization/project scope
+- **Team Isolation**: Maintain data separation between different teams and projects for secure collaboration
+
+Example with the mem0 Python package:
+
+```python
+from mem0 import MemoryClient
+
+# Recommended: Using organization and project IDs
+client = MemoryClient(
+    org_id='YOUR_ORG_ID', # It can be found on the organization settings page in dashboard
+    project_id='YOUR_PROJECT_ID',
+)
+```
+> **Note**: The use of `organization` and `project` parameters is deprecated and will be removed in version `0.1.40`. Please use `org_id` and `project_id` instead.
+
+
+Example with the mem0 Node.js package:
+
+```javascript
+import { MemoryClient } from "mem0ai";
+
+# Recommended: Using organization and project IDs
+const client = new MemoryClient({
+    organizationId: "YOUR_ORG_ID",
+    projectId: "YOUR_PROJECT_ID"
+});
+```
+
+## Getting Started
+
+To begin using the Mem0 API, you'll need to:
+
+1. Sign up for a [Mem0 account](https://app.mem0.ai) and obtain your API key.
+2. Familiarize yourself with the API endpoints and their functionalities.
+3. Make your first API call to add or retrieve a memory.
+
+Explore the detailed documentation for each API endpoint to learn more about request/response formats, parameters, and example usage.

docs/api-reference/project/add-project-member.mdx

@@ -0,0 +1,9 @@
+---
+title: 'Add Member'
+openapi: post /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/
+---
+
+The API provides two roles for project members:
+
+- `READER`: Allows viewing of project resources.
+- `OWNER`: Grants full administrative access to manage the project and its resources.

docs/api-reference/project/create-project.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Create Project'
+openapi: post /api/v1/orgs/organizations/{org_id}/projects/
+---

docs/api-reference/project/delete-project-member.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Delete Member'
+openapi: delete /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/
+---

docs/api-reference/project/delete-project.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Delete Project'
+openapi: delete /api/v1/orgs/organizations/{org_id}/projects/{project_id}/
+---

docs/api-reference/project/get-project-members.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Get Members'
+openapi: get /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/
+---

docs/api-reference/project/get-project.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Get Project'
+openapi: get /api/v1/orgs/organizations/{org_id}/projects/{project_id}/
+---

docs/api-reference/project/get-projects.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Get Projects'
+openapi: get /api/v1/orgs/organizations/{org_id}/projects/
+---

docs/api-reference/project/update-project-member.mdx

@@ -0,0 +1,9 @@
+---
+title: 'Update Member'
+openapi: put /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/
+---
+
+The API provides two roles for project members:
+
+- `READER`: Allows viewing of project resources.
+- `OWNER`: Grants full administrative access to manage the project and its resources.

docs/api-reference/project/update-project.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Update Project'
+openapi: patch /api/v1/orgs/organizations/{org_id}/projects/{project_id}/
+---

docs/components/embedders/config.mdx

@@ -0,0 +1,61 @@
+## What is Config?
+
+Config in mem0 is a dictionary that specifies the settings for your embedding models. It allows you to customize the behavior and connection details of your chosen embedder.
+
+## How to Define Config
+
+The config is defined as a Python dictionary with two main keys:
+- `embedder`: Specifies the embedder provider and its configuration
+  - `provider`: The name of the embedder (e.g., "openai", "ollama")
+  - `config`: A nested dictionary containing provider-specific settings
+
+## How to Use Config
+
+Here's a general example of how to use the config with mem0:
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "embedder": {
+        "provider": "your_chosen_provider",
+        "config": {
+            # Provider-specific settings go here
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Your text here", user_id="user", metadata={"category": "example"})
+```
+
+## Why is Config Needed?
+
+Config is essential for:
+1. Specifying which embedding model to use.
+2. Providing necessary connection details (e.g., model, api_key, embedding_dims).
+3. Ensuring proper initialization and connection to your chosen embedder.
+
+## Master List of All Params in Config
+
+Here's a comprehensive list of all parameters that can be used across different embedders:
+
+| Parameter | Description |
+|-----------|-------------|
+| `model` | Embedding model to use |
+| `api_key` | API key of the provider |
+| `embedding_dims` | Dimensions of the embedding model |
+| `http_client_proxies` | Allow proxy server settings |
+| `ollama_base_url` | Base URL for the Ollama embedding model |
+| `model_kwargs` | Key-Value arguments for the Huggingface embedding model |
+| `azure_kwargs` | Key-Value arguments for the AzureOpenAI embedding model |
+| `openai_base_url`    | Base URL for OpenAI API                       | OpenAI            |
+| `vertex_credentials_json` | Path to the Google Cloud credentials JSON file for VertexAI |
+
+
+## Supported Embedding Models
+
+For detailed information on configuring specific embedders, please visit the [Embedding Models](./models) section. There you'll find information for each supported embedder with provider-specific usage examples and configuration details.

docs/components/embedders/models/azure_openai.mdx

@@ -0,0 +1,51 @@
+---
+title: Azure OpenAI
+---
+
+To use Azure OpenAI embedding models, set the `EMBEDDING_AZURE_OPENAI_API_KEY`, `EMBEDDING_AZURE_DEPLOYMENT`, `EMBEDDING_AZURE_ENDPOINT` and `EMBEDDING_AZURE_API_VERSION` environment variables. You can obtain the Azure OpenAI API key from the Azure.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["EMBEDDING_AZURE_OPENAI_API_KEY"] = "your-api-key"
+os.environ["EMBEDDING_AZURE_DEPLOYMENT"] = "your-deployment-name"
+os.environ["EMBEDDING_AZURE_ENDPOINT"] = "your-api-base-url"
+os.environ["EMBEDDING_AZURE_API_VERSION"] = "version-to-use"
+
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+
+config = {
+    "embedder": {
+        "provider": "azure_openai",
+        "config": {
+            "model": "text-embedding-3-large"
+            "azure_kwargs": {
+                  "api_version": "",
+                  "azure_deployment": "",
+                  "azure_endpoint": "",
+                  "api_key": "",
+                  "default_headers": {
+                    "CustomHeader": "your-custom-header",
+                  }
+              }
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("I'm visiting Paris", user_id="john")
+```
+
+### Config
+
+Here are the parameters available for configuring Azure OpenAI embedder:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the embedding model to use | `text-embedding-3-small` |
+| `embedding_dims` | Dimensions of the embedding model | `1536` |
+| `azure_kwargs` | The Azure OpenAI configs | `config_keys` |

docs/components/embedders/models/gemini.mdx

@@ -0,0 +1,37 @@
+---
+title: Gemini
+---
+
+To use Gemini embedding models, set the `GOOGLE_API_KEY` environment variables. You can obtain the Gemini API key from [here](https://aistudio.google.com/app/apikey).
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["GOOGLE_API_KEY"] = "key"
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+config = {
+    "embedder": {
+        "provider": "gemini",
+        "config": {
+            "model": "models/text-embedding-004",
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("I'm visiting Paris", user_id="john")
+```
+
+### Config
+
+Here are the parameters available for configuring Gemini embedder:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the embedding model to use | `models/text-embedding-004` |
+| `embedding_dims` | Dimensions of the embedding model | `768` |
+| `api_key` | The Gemini API key | `None` |

docs/components/embedders/models/huggingface.mdx

@@ -0,0 +1,36 @@
+---
+title: Hugging Face
+---
+
+You can use embedding models from Huggingface to run Mem0 locally.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+config = {
+    "embedder": {
+        "provider": "huggingface",
+        "config": {
+            "model": "multi-qa-MiniLM-L6-cos-v1"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("I'm visiting Paris", user_id="john")
+```
+
+### Config
+
+Here are the parameters available for configuring Huggingface embedder:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the model to use | `multi-qa-MiniLM-L6-cos-v1` |
+| `embedding_dims` | Dimensions of the embedding model | `selected_model_dimensions` |
+| `model_kwargs` | Additional arguments for the model | `None` |

docs/components/embedders/models/ollama.mdx

@@ -0,0 +1,32 @@
+You can use embedding models from Ollama to run Mem0 locally.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+config = {
+    "embedder": {
+        "provider": "ollama",
+        "config": {
+            "model": "mxbai-embed-large"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("I'm visiting Paris", user_id="john")
+```
+
+### Config
+
+Here are the parameters available for configuring Ollama embedder:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the OpenAI model to use | `nomic-embed-text` |
+| `embedding_dims` | Dimensions of the embedding model | `512` |
+| `ollama_base_url` | Base URL for ollama connection | `None` |

docs/components/embedders/models/openai.mdx

@@ -0,0 +1,36 @@
+---
+title: OpenAI
+---
+
+To use OpenAI embedding models, set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your_api_key"
+
+config = {
+    "embedder": {
+        "provider": "openai",
+        "config": {
+            "model": "text-embedding-3-large"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("I'm visiting Paris", user_id="john")
+```
+
+### Config
+
+Here are the parameters available for configuring OpenAI embedder:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the embedding model to use | `text-embedding-3-small` |
+| `embedding_dims` | Dimensions of the embedding model | `1536` |
+| `api_key` | The OpenAI API key | `None` |

docs/components/embedders/models/together.mdx

@@ -0,0 +1,39 @@
+---
+title: Together
+---
+
+To use Together embedding models, set the `TOGETHER_API_KEY` environment variable. You can obtain the Together API key from the [Together Platform](https://api.together.xyz/settings/api-keys).
+
+### Usage
+
+<Note> The `embedding_model_dims` parameter for `vector_store` should be set to `768` for Together embedder. </Note>
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["TOGETHER_API_KEY"] = "your_api_key"
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+config = {
+    "embedder": {
+        "provider": "together",
+        "config": {
+            "model": "togethercomputer/m2-bert-80M-8k-retrieval"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("I'm visiting Paris", user_id="john")
+```
+
+### Config
+
+Here are the parameters available for configuring Together embedder:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the embedding model to use | `togethercomputer/m2-bert-80M-8k-retrieval` |
+| `embedding_dims` | Dimensions of the embedding model | `768` |
+| `api_key` | The Together API key | `None` |

docs/components/embedders/models/vertexai.mdx

@@ -0,0 +1,36 @@
+### Vertex AI
+
+To use Google Cloud's Vertex AI for text embedding models, set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to point to the path of your service account's credentials JSON file. These credentials can be created in the [Google Cloud Console](https://console.cloud.google.com/).
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+# Set the path to your Google Cloud credentials JSON file
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/path/to/your/credentials.json"
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+config = {
+    "embedder": {
+        "provider": "vertexai",
+        "config": {
+            "model": "text-embedding-004"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("I'm visiting Paris", user_id="john")
+```
+
+### Config
+
+Here are the parameters available for configuring the Vertex AI embedder:
+
+| Parameter                 | Description                                      | Default Value        |
+| ------------------------- | ------------------------------------------------ | -------------------- |
+| `model`                   | The name of the Vertex AI embedding model to use | `text-embedding-004` |
+| `vertex_credentials_json` | Path to the Google Cloud credentials JSON file   | `None`               |
+| `embedding_dims`          | Dimensions of the embedding model                | `256`                |

docs/components/embedders/overview.mdx

@@ -0,0 +1,25 @@
+---
+title: Overview
+---
+
+Mem0 offers support for various embedding models, allowing users to choose the one that best suits their needs.
+
+## Supported Embedders
+
+See the list of supported embedders below.
+
+<CardGroup cols={4}>
+  <Card title="OpenAI" href="/components/embedders/models/openai"></Card>
+  <Card title="Azure OpenAI" href="/components/embedders/models/azure_openai"></Card>
+  <Card title="Ollama" href="/components/embedders/models/ollama"></Card>
+  <Card title="Hugging Face" href="/components/embedders/models/huggingface"></Card>
+  <Card title="Gemini" href="/components/embedders/models/gemini"></Card>
+  <Card title="Vertex AI" href="/components/embedders/models/vertexai"></Card>
+  <Card title="Together" href="/components/embedders/models/together"></Card>
+</CardGroup>
+
+## Usage
+
+To utilize a embedder, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and `OpenAI` will be used as the embedder.
+
+For a comprehensive list of available parameters for embedder configuration, please refer to [Config](./config).

docs/components/embedding-models.mdx

@@ -1,222 +0,0 @@
----
-title: 🧩 Embedding models
----
-
-## Overview
-
-Embedchain supports several embedding models from the following providers:
-
-<CardGroup cols={4}>
-  <Card title="OpenAI" href="#openai"></Card>
-  <Card title="GoogleAI" href="#google-ai"></Card>
-  <Card title="Azure OpenAI" href="#azure-openai"></Card>
-  <Card title="GPT4All" href="#gpt4all"></Card>
-  <Card title="Hugging Face" href="#hugging-face"></Card>
-  <Card title="Vertex AI" href="#vertex-ai"></Card>
-</CardGroup>
-
-## OpenAI
-
-To use OpenAI embedding function, you have to set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
-
-Once you have obtained the key, you can use it like this:
-
-<CodeGroup>
-
-```python main.py
-import os
-from embedchain import App
-
-os.environ['OPENAI_API_KEY'] = 'xxx'
-
-# load embedding model configuration from config.yaml file
-app = App.from_config(config_path="config.yaml")
-
-app.add("https://en.wikipedia.org/wiki/OpenAI")
-app.query("What is OpenAI?")
-```
-
-```yaml config.yaml
-embedder:
-  provider: openai
-  config:
-    model: 'text-embedding-3-small'
-```
-
-</CodeGroup>
-
-* OpenAI announced two new embedding models: `text-embedding-3-small` and `text-embedding-3-large`. Embedchain supports both these models. Below you can find YAML config for both:
-
-<CodeGroup>
-
-```yaml text-embedding-3-small.yaml
-embedder:
-  provider: openai
-  config:
-    model: 'text-embedding-3-small'
-```
-
-```yaml text-embedding-3-large.yaml
-embedder:
-  provider: openai
-  config:
-    model: 'text-embedding-3-large'
-```
-
-</CodeGroup>
-
-## Google AI
-
-To use Google AI embedding function, you have to set the `GOOGLE_API_KEY` environment variable. You can obtain the Google API key from the [Google Maker Suite](https://makersuite.google.com/app/apikey)
-
-<CodeGroup>
-```python main.py
-import os
-from embedchain import App
-
-os.environ["GOOGLE_API_KEY"] = "xxx"
-
-app = App.from_config(config_path="config.yaml")
-```
-
-```yaml config.yaml
-embedder:
-  provider: google
-  config:
-    model: 'models/embedding-001'
-    task_type: "retrieval_document"
-    title: "Embeddings for Embedchain"
-```
-</CodeGroup>
-<br/>
-<Note>
-For more details regarding the Google AI embedding model, please refer to the [Google AI documentation](https://ai.google.dev/tutorials/python_quickstart#use_embeddings).
-</Note>
-
-## Azure OpenAI
-
-To use Azure OpenAI embedding model, you have to set some of the azure openai related environment variables as given in the code block below:
-
-<CodeGroup>
-
-```python main.py
-import os
-from embedchain import App
-
-os.environ["OPENAI_API_TYPE"] = "azure"
-os.environ["AZURE_OPENAI_ENDPOINT"] = "https://xxx.openai.azure.com/"
-os.environ["AZURE_OPENAI_API_KEY"] = "xxx"
-os.environ["OPENAI_API_VERSION"] = "xxx"
-
-app = App.from_config(config_path="config.yaml")
-```
-
-```yaml config.yaml
-llm:
-  provider: azure_openai
-  config:
-    model: gpt-35-turbo
-    deployment_name: your_llm_deployment_name
-    temperature: 0.5
-    max_tokens: 1000
-    top_p: 1
-    stream: false
-
-embedder:
-  provider: azure_openai
-  config:
-    model: text-embedding-ada-002
-    deployment_name: you_embedding_model_deployment_name
-```
-</CodeGroup>
-
-You can find the list of models and deployment name on the [Azure OpenAI Platform](https://oai.azure.com/portal).
-
-## GPT4ALL
-
-GPT4All supports generating high quality embeddings of arbitrary length documents of text using a CPU optimized contrastively trained Sentence Transformer.
-
-<CodeGroup>
-
-```python main.py
-from embedchain import App
-
-# load embedding model configuration from config.yaml file
-app = App.from_config(config_path="config.yaml")
-```
-
-```yaml config.yaml
-llm:
-  provider: gpt4all
-  config:
-    model: 'orca-mini-3b-gguf2-q4_0.gguf'
-    temperature: 0.5
-    max_tokens: 1000
-    top_p: 1
-    stream: false
-
-embedder:
-  provider: gpt4all
-```
-
-</CodeGroup>
-
-## Hugging Face
-
-Hugging Face supports generating embeddings of arbitrary length documents of text using Sentence Transformer library. Example of how to generate embeddings using hugging face is given below:
-
-<CodeGroup>
-
-```python main.py
-from embedchain import App
-
-# load embedding model configuration from config.yaml file
-app = App.from_config(config_path="config.yaml")
-```
-
-```yaml config.yaml
-llm:
-  provider: huggingface
-  config:
-    model: 'google/flan-t5-xxl'
-    temperature: 0.5
-    max_tokens: 1000
-    top_p: 0.5
-    stream: false
-
-embedder:
-  provider: huggingface
-  config:
-    model: 'sentence-transformers/all-mpnet-base-v2'
-```
-
-</CodeGroup>
-
-## Vertex AI
-
-Embedchain supports Google's VertexAI embeddings model through a simple interface. You just have to pass the `model_name` in the config yaml and it would work out of the box.
-
-<CodeGroup>
-
-```python main.py
-from embedchain import App
-
-# load embedding model configuration from config.yaml file
-app = App.from_config(config_path="config.yaml")
-```
-
-```yaml config.yaml
-llm:
-  provider: vertexai
-  config:
-    model: 'chat-bison'
-    temperature: 0.5
-    top_p: 0.5
-
-embedder:
-  provider: vertexai
-  config:
-    model: 'textembedding-gecko'
-```
-
-</CodeGroup>

docs/components/llms/config.mdx

@@ -0,0 +1,79 @@
+## What is Config?
+
+Config in mem0 is a dictionary that specifies the settings for your llms. It allows you to customize the behavior and connection details of your chosen llm.
+
+## How to Define Config
+
+The config is defined as a Python dictionary with two main keys:
+- `llm`: Specifies the llm provider and its configuration
+  - `provider`: The name of the llm (e.g., "openai", "groq")
+  - `config`: A nested dictionary containing provider-specific settings
+
+### Config Values Precedence
+
+Config values are applied in the following order of precedence (from highest to lowest):
+
+1. Values explicitly set in the `config` dictionary
+2. Environment variables (e.g., `OPENAI_API_KEY`, `OPENAI_API_BASE`)
+3. Default values defined in the LLM implementation
+
+This means that values specified in the `config` dictionary will override corresponding environment variables, which in turn override default values.
+
+## How to Use Config
+
+Here's a general example of how to use the config with mem0:
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx" # for embedder
+
+config = {
+    "llm": {
+        "provider": "your_chosen_provider",
+        "config": {
+            # Provider-specific settings go here
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Your text here", user_id="user", metadata={"category": "example"})
+```
+
+## Why is Config Needed?
+
+Config is essential for:
+1. Specifying which llm to use.
+2. Providing necessary connection details (e.g., model, api_key, temperature).
+3. Ensuring proper initialization and connection to your chosen llm.
+
+## Master List of All Params in Config
+
+Here's a comprehensive list of all parameters that can be used across different llms:
+
+Here's the table based on the provided parameters:
+
+| Parameter            | Description                                   | Provider          |
+|----------------------|-----------------------------------------------|-------------------|
+| `model`              | Embedding model to use                        | All               |
+| `temperature`        | Temperature of the model                      | All               |
+| `api_key`            | API key to use                                | All               |
+| `max_tokens`         | Tokens to generate                            | All               |
+| `top_p`              | Probability threshold for nucleus sampling    | All               |
+| `top_k`              | Number of highest probability tokens to keep  | All               |
+| `http_client_proxies`| Allow proxy server settings                   | AzureOpenAI       |
+| `models`             | List of models                                | Openrouter        |
+| `route`              | Routing strategy                              | Openrouter        |
+| `openrouter_base_url`| Base URL for Openrouter API                   | Openrouter        |
+| `site_url`           | Site URL                                      | Openrouter        |
+| `app_name`           | Application name                              | Openrouter        |
+| `ollama_base_url`    | Base URL for Ollama API                       | Ollama            |
+| `openai_base_url`    | Base URL for OpenAI API                       | OpenAI            |
+| `azure_kwargs`       | Azure LLM args for initialization             | AzureOpenAI       |
+
+
+## Supported LLMs
+
+For detailed information on configuring specific llms, please visit the [LLMs](./models) section. There you'll find information for each supported llm with provider-specific usage examples and configuration details.

docs/components/llms/models/anthropic.mdx

@@ -0,0 +1,29 @@
+To use anthropic's models, please set the `ANTHROPIC_API_KEY` which you find on their [Account Settings Page](https://console.anthropic.com/account/keys).
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
+os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
+
+config = {
+    "llm": {
+        "provider": "anthropic",
+        "config": {
+            "model": "claude-3-5-sonnet-latest",
+            "temperature": 0.1,
+            "max_tokens": 2000,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+## Config
+
+All available parameters for the `anthropic` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/aws_bedrock.mdx

@@ -0,0 +1,38 @@
+---
+title: AWS Bedrock
+---
+
+### Setup
+- Before using the AWS Bedrock LLM, make sure you have the appropriate model access from [Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess).
+- You will also need to authenticate the `boto3` client by using a method in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials)
+- You will have to export `AWS_REGION`, `AWS_ACCESS_KEY`, and `AWS_SECRET_ACCESS_KEY` to set environment variables.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
+os.environ['AWS_REGION'] = 'us-east-1'
+os.environ["AWS_ACCESS_KEY"] = "xx"
+os.environ["AWS_SECRET_ACCESS_KEY"] = "xx"
+
+config = {
+    "llm": {
+        "provider": "aws_bedrock",
+        "config": {
+            "model": "arn:aws:bedrock:us-east-1:123456789012:model/your-model-name",
+            "temperature": 0.2,
+            "max_tokens": 1500,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+### Config
+
+All available parameters for the `aws_bedrock` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/azure_openai.mdx

@@ -0,0 +1,76 @@
+---
+title: Azure OpenAI
+---
+
+To use Azure OpenAI models, you have to set the `LLM_AZURE_OPENAI_API_KEY`, `LLM_AZURE_ENDPOINT`, `LLM_AZURE_DEPLOYMENT` and `LLM_AZURE_API_VERSION` environment variables. You can obtain the Azure API key from the [Azure](https://azure.microsoft.com/).
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["LLM_AZURE_OPENAI_API_KEY"] = "your-api-key"
+os.environ["LLM_AZURE_DEPLOYMENT"] = "your-deployment-name"
+os.environ["LLM_AZURE_ENDPOINT"] = "your-api-base-url"
+os.environ["LLM_AZURE_API_VERSION"] = "version-to-use"
+
+config = {
+    "llm": {
+        "provider": "azure_openai",
+        "config": {
+            "model": "your-deployment-name",
+            "temperature": 0.1,
+            "max_tokens": 2000,
+            "azure_kwargs": {
+                  "azure_deployment": "",
+                  "api_version": "",
+                  "azure_endpoint": "",
+                  "api_key": "",
+                  "default_headers": {
+                    "CustomHeader": "your-custom-header",
+                  }
+              }
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+We also support the new [OpenAI structured-outputs](https://platform.openai.com/docs/guides/structured-outputs/introduction) model.
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["LLM_AZURE_OPENAI_API_KEY"] = "your-api-key"
+os.environ["LLM_AZURE_DEPLOYMENT"] = "your-deployment-name"
+os.environ["LLM_AZURE_ENDPOINT"] = "your-api-base-url"
+os.environ["LLM_AZURE_API_VERSION"] = "version-to-use"
+
+config = {
+    "llm": {
+        "provider": "azure_openai_structured",
+        "config": {
+            "model": "your-deployment-name",
+            "temperature": 0.1,
+            "max_tokens": 2000,
+            "azure_kwargs": {
+                  "azure_deployment": "",
+                  "api_version": "",
+                  "azure_endpoint": "",
+                  "api_key": "",
+                  "default_headers": {
+                    "CustomHeader": "your-custom-header",
+                  }
+              }
+        }
+    }
+}
+```
+
+## Config
+
+All available parameters for the `azure_openai` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/gemini.mdx

@@ -0,0 +1,33 @@
+---
+title: Gemini
+---
+
+To use Gemini model, you have to set the `GEMINI_API_KEY` environment variable. You can obtain the Gemini API key from the [Google AI Studio](https://aistudio.google.com/app/apikey)
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
+os.environ["GEMINI_API_KEY"] = "your-api-key"
+
+config = {
+    "llm": {
+        "provider": "gemini",
+        "config": {
+            "model": "gemini-1.5-flash-latest",
+            "temperature": 0.2,
+            "max_tokens": 1500,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+## Config
+
+All available parameters for the `Gemini` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/google_AI.mdx

@@ -0,0 +1,33 @@
+---
+title: Google AI
+---
+
+To use Google AI model, you have to set the `GOOGLE_API_KEY` environment variable. You can obtain the Google API key from the [Google Maker Suite](https://makersuite.google.com/app/apikey)
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
+os.environ["GEMINI_API_KEY"] = "your-api-key"
+
+config = {
+    "llm": {
+        "provider": "litellm",
+        "config": {
+            "model": "gemini/gemini-pro",
+            "temperature": 0.2,
+            "max_tokens": 1500,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+## Config
+
+All available parameters for the `litellm` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/groq.mdx

@@ -0,0 +1,31 @@
+[Groq](https://groq.com/) is the creator of the world's first Language Processing Unit (LPU), providing exceptional speed performance for AI workloads running on their LPU Inference Engine.
+
+In order to use LLMs from Groq, go to their [platform](https://console.groq.com/keys) and get the API key. Set the API key as `GROQ_API_KEY` environment variable to use the model as given below in the example.
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
+os.environ["GROQ_API_KEY"] = "your-api-key"
+
+config = {
+    "llm": {
+        "provider": "groq",
+        "config": {
+            "model": "mixtral-8x7b-32768",
+            "temperature": 0.1,
+            "max_tokens": 1000,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+## Config
+
+All available parameters for the `groq` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/litellm.mdx

@@ -0,0 +1,28 @@
+[Litellm](https://litellm.vercel.app/docs/) is compatible with over 100 large language models (LLMs), all using a standardized input/output format. You can explore the [available models](https://litellm.vercel.app/docs/providers) to use with Litellm. Ensure you set the `API_KEY` for the model you choose to use.
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+config = {
+    "llm": {
+        "provider": "litellm",
+        "config": {
+            "model": "gpt-4o-mini",
+            "temperature": 0.2,
+            "max_tokens": 1500,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+## Config
+
+All available parameters for the `litellm` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/mistral_AI.mdx

@@ -0,0 +1,33 @@
+---
+title: Mistral AI
+---
+
+To use mistral's models, please Obtain the Mistral AI api key from their [console](https://console.mistral.ai/). Set the `MISTRAL_API_KEY` environment variable to use the model as given below in the example.
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
+os.environ["MISTRAL_API_KEY"] = "your-api-key"
+
+config = {
+    "llm": {
+        "provider": "litellm",
+        "config": {
+            "model": "open-mixtral-8x7b",
+            "temperature": 0.1,
+            "max_tokens": 2000,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+## Config
+
+All available parameters for the `litellm` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/ollama.mdx

@@ -0,0 +1,28 @@
+You can use LLMs from Ollama to run Mem0 locally. These [models](https://ollama.com/search?c=tools) support tool support.
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key" # for embedder
+
+config = {
+    "llm": {
+        "provider": "ollama",
+        "config": {
+            "model": "mixtral:8x7b",
+            "temperature": 0.1,
+            "max_tokens": 2000,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+## Config
+
+All available parameters for the `ollama` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/openai.mdx

@@ -0,0 +1,66 @@
+---
+title: OpenAI
+---
+
+To use OpenAI LLM models, you have to set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+config = {
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "model": "gpt-4o",
+            "temperature": 0.2,
+            "max_tokens": 1500,
+        }
+    }
+}
+
+# Use Openrouter by passing it's api key
+# os.environ["OPENROUTER_API_KEY"] = "your-api-key"
+# config = {
+#    "llm": {
+#        "provider": "openai",
+#        "config": {
+#            "model": "meta-llama/llama-3.1-70b-instruct",
+#        }
+#    }
+# }
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+We also support the new [OpenAI structured-outputs](https://platform.openai.com/docs/guides/structured-outputs/introduction) model.
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+config = {
+    "llm": {
+        "provider": "openai_structured",
+        "config": {
+            "model": "gpt-4o-2024-08-06",
+            "temperature": 0.0,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+```
+
+
+
+## Config
+
+All available parameters for the `openai` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/together.mdx

@@ -0,0 +1,29 @@
+To use TogetherAI LLM models, you have to set the `TOGETHER_API_KEY` environment variable. You can obtain the TogetherAI API key from their [Account settings page](https://api.together.xyz/settings/api-keys).
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
+os.environ["TOGETHER_API_KEY"] = "your-api-key"
+
+config = {
+    "llm": {
+        "provider": "together",
+        "config": {
+            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+            "temperature": 0.2,
+            "max_tokens": 1500,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+## Config
+
+All available parameters for the `togetherai` config are present in [Master List of All Params in Config](../config).

docs/components/llms/overview.mdx

@@ -0,0 +1,51 @@
+---
+title: Overview
+---
+
+Mem0 includes built-in support for various popular large language models. Memory can utilize the LLM provided by the user, ensuring efficient use for specific needs.
+
+## Usage
+
+To use a llm, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and `OpenAI` will be used as the llm.
+
+For a comprehensive list of available parameters for llm configuration, please refer to [Config](./config).
+
+To view all supported llms, visit the [Supported LLMs](./models).
+
+<CardGroup cols={4}>
+  <Card title="OpenAI" href="/components/llms/models/openai"></Card>
+    <Card title="Ollama" href="/components/llms/models/ollama"></Card>
+    <Card title="Azure OpenAI" href="/components/llms/models/azure_openai"></Card>
+    <Card title="Anthropic" href="/components/llms/models/anthropic"></Card>
+    <Card title="Together" href="/components/llms/models/together"></Card>
+    <Card title="Groq" href="/components/llms/models/groq"></Card>
+    <Card title="Litellm" href="/components/llms/models/litellm"></Card>
+    <Card title="Mistral AI" href="/components/llms/models/mistral_ai"></Card>
+    <Card title="Google AI" href="/components/llms/models/google_ai"></Card>
+    <Card title="AWS bedrock" href="/components/llms/models/aws_bedrock"></Card>
+    <Card title="Gemini" href="/components/llms/models/gemini"></Card>
+</CardGroup>
+
+## Structured vs Unstructured Outputs
+
+Mem0 supports two types of OpenAI LLM formats, each with its own strengths and use cases:
+
+### Structured Outputs
+
+Structured outputs are LLMs that align with OpenAI's structured outputs model:
+
+- **Optimized for:** Returning structured responses (e.g., JSON objects)
+- **Benefits:** Precise, easily parseable data
+- **Ideal for:** Data extraction, form filling, API responses
+- **Learn more:** [OpenAI Structured Outputs Guide](https://platform.openai.com/docs/guides/structured-outputs/introduction)
+
+### Unstructured Outputs
+
+Unstructured outputs correspond to OpenAI's standard, free-form text model:
+
+- **Flexibility:** Returns open-ended, natural language responses
+- **Customization:** Use the `response_format` parameter to guide output
+- **Trade-off:** Less efficient than structured outputs for specific data needs
+- **Best for:** Creative writing, explanations, general conversation
+
+Choose the format that best suits your application's requirements for optimal performance and usability.

docs/components/vectordbs/config.mdx

@@ -0,0 +1,72 @@
+## What is Config?
+
+Config in mem0 is a dictionary that specifies the settings for your vector database. It allows you to customize the behavior and connection details of your chosen vector store.
+
+## How to Define Config
+
+The config is defined as a Python dictionary with two main keys:
+- `vector_store`: Specifies the vector database provider and its configuration
+  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus","azure_ai_search")
+  - `config`: A nested dictionary containing provider-specific settings
+
+## How to Use Config
+
+Here's a general example of how to use the config with mem0:
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "your_chosen_provider",
+        "config": {
+            # Provider-specific settings go here
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Your text here", user_id="user", metadata={"category": "example"})
+```
+
+## Why is Config Needed?
+
+Config is essential for:
+1. Specifying which vector database to use.
+2. Providing necessary connection details (e.g., host, port, credentials).
+3. Customizing database-specific settings (e.g., collection name, path).
+4. Ensuring proper initialization and connection to your chosen vector store.
+
+## Master List of All Params in Config
+
+Here's a comprehensive list of all parameters that can be used across different vector databases:
+
+| Parameter | Description |
+|-----------|-------------|
+| `collection_name` | Name of the collection |
+| `embedding_model_dims` | Dimensions of the embedding model |
+| `client` | Custom client for the database |
+| `path` | Path for the database |
+| `host` | Host where the server is running |
+| `port` | Port where the server is running |
+| `user` | Username for database connection |
+| `password` | Password for database connection |
+| `dbname` | Name of the database |
+| `url` | Full URL for the server |
+| `api_key` | API key for the server |
+| `on_disk` | Enable persistent storage |
+
+## Customizing Config
+
+Each vector database has its own specific configuration requirements. To customize the config for your chosen vector store:
+
+1. Identify the vector database you want to use from [supported vector databases](./dbs).
+2. Refer to the `Config` section in the respective vector database's documentation.
+3. Include only the relevant parameters for your chosen database in the `config` dictionary.
+
+## Supported Vector Databases
+
+For detailed information on configuring specific vector databases, please visit the [Supported Vector Databases](./dbs) section. There you'll find individual pages for each supported vector store with provider-specific usage examples and configuration details.

docs/components/vectordbs/dbs/azure_ai_search.mdx

@@ -0,0 +1,38 @@
+[Azure AI Search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search/) (formerly known as "Azure Cognitive Search") provides secure information retrieval at scale over user-owned content in traditional and generative AI search applications.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"   #this key is used for embedding purpose
+
+config = {
+    "vector_store": {
+        "provider": "azure_ai_search",
+        "config": {
+            "service_name": "ai-search-test",
+            "api_key": "*****",
+            "collection_name": "mem0", 
+            "embedding_model_dims": 1536 ,
+            "use_compression": False
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+### Config
+
+Let's see the available parameters for the `qdrant` config:
+service_name (str): Azure Cognitive Search service name.  
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `service_name` | Azure AI Search service name | `None` |
+| `api_key` | API key of the Azure AI Search service | `None` |
+| `collection_name` | The name of the collection/index to store the vectors, it will be created automatically if not exist | `mem0` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `use_compression` | Use scalar quantization vector compression | False |

docs/components/vectordbs/dbs/chroma.mdx

@@ -0,0 +1,35 @@
+[Chroma](https://www.trychroma.com/) is an AI-native open-source vector database that simplifies building LLM apps by providing tools for storing, embedding, and searching embeddings with a focus on simplicity and speed.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "test",
+            "path": "db",
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+### Config
+
+Here are the parameters available for configuring Chroma:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collection_name` | The name of the collection | `mem0` |
+| `client` | Custom client for Chroma | `None` |
+| `path` | Path for the Chroma database | `db` |
+| `host` | The host where the Chroma server is running | `None` |
+| `port` | The port where the Chroma server is running | `None` |

docs/components/vectordbs/dbs/elasticsearch.mdx

@@ -0,0 +1,58 @@
+[Elasticsearch](https://www.elastic.co/) is a distributed, RESTful search and analytics engine that can efficiently store and search vector data using dense vectors and k-NN search.
+
+### Installation
+
+Elasticsearch support requires additional dependencies. Install them with:
+
+```bash
+pip install elasticsearch>=8.0.0
+```
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "elasticsearch",
+        "config": {
+            "collection_name": "mem0",
+            "host": "localhost",
+            "port": 9200,
+            "embedding_model_dims": 1536
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+### Config
+
+Let's see the available parameters for the `elasticsearch` config:
+
+| Parameter              | Description                                        | Default Value |
+| ---------------------- | -------------------------------------------------- | ------------- |
+| `collection_name`      | The name of the index to store the vectors         | `mem0`        |
+| `embedding_model_dims` | Dimensions of the embedding model                  | `1536`        |
+| `host`                 | The host where the Elasticsearch server is running | `localhost`   |
+| `port`                 | The port where the Elasticsearch server is running | `9200`        |
+| `cloud_id`             | Cloud ID for Elastic Cloud deployment              | `None`        |
+| `api_key`              | API key for authentication                         | `None`        |
+| `user`                 | Username for basic authentication                  | `None`        |
+| `password`             | Password for basic authentication                  | `None`        |
+| `verify_certs`         | Whether to verify SSL certificates                 | `True`        |
+| `auto_create_index`    | Whether to automatically create the index          | `True`        |
+
+### Features
+
+- Efficient vector search using Elasticsearch's native k-NN search
+- Support for both local and cloud deployments (Elastic Cloud)
+- Multiple authentication methods (Basic Auth, API Key)
+- Automatic index creation with optimized mappings for vector search
+- Memory isolation through payload filtering

docs/components/vectordbs/dbs/milvus.mdx

@@ -0,0 +1,35 @@
+[Milvus](https://milvus.io/) Milvus is an open-source vector database that suits AI applications of every size from running a demo chatbot in Jupyter notebook to building web-scale search that serves billions of users.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+config = {
+    "vector_store": {
+        "provider": "milvus",
+        "config": {
+            "collection_name": "test",
+            "embedding_model_dims": "123",
+            "url": "127.0.0.1",
+            "token": "8e4b8ca8cf2c67",
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+### Config
+
+Here's the parameters available for configuring Milvus Database:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `url` | Full URL/Uri for Milvus/Zilliz server | `http://localhost:19530` |
+| `token` | Token for Zilliz server / for local setup defaults to None. | `None` |
+| `collection_name` | The name of the collection | `mem0` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `metric_type` | Metric type for similarity search | `L2` |

docs/components/vectordbs/dbs/pgvector.mdx

@@ -0,0 +1,41 @@
+[pgvector](https://github.com/pgvector/pgvector) is open-source vector similarity search for Postgres. After connecting with postgres run `CREATE EXTENSION IF NOT EXISTS vector;` to create the vector extension.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "pgvector",
+        "config": {
+            "user": "test",
+            "password": "123",
+            "host": "127.0.0.1",
+            "port": "5432",
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+### Config
+
+Here's the parameters available for configuring pgvector:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `dbname` | The name of the  | `postgres` |
+| `collection_name` | The name of the collection | `mem0` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `user` | User name to connect to the database | `None` |
+| `password` | Password to connect to the database | `None` |
+| `host` | The host where the Postgres server is running | `None` |
+| `port` | The port where the Postgres server is running | `None` |
+| `diskann` | Whether to use diskann for vector similarity search (requires pgvectorscale) | `True` |
+| `hnsw` | Whether to use hnsw for vector similarity search | `False` |

docs/components/vectordbs/dbs/qdrant.mdx

@@ -0,0 +1,40 @@
+[Qdrant](https://qdrant.tech/) is an open-source vector search engine. It is designed to work with large-scale datasets and provides a high-performance search engine for vector data.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "qdrant",
+        "config": {
+            "collection_name": "test",
+            "host": "localhost",
+            "port": 6333,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+### Config
+
+Let's see the available parameters for the `qdrant` config:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collection_name` | The name of the collection to store the vectors | `mem0` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `client` | Custom client for qdrant | `None` |
+| `host` | The host where the qdrant server is running | `None` |
+| `port` | The port where the qdrant server is running | `None` |
+| `path` | Path for the qdrant database | `/tmp/qdrant` |
+| `url` | Full URL for the qdrant server | `None` |
+| `api_key` | API key for the qdrant server | `None` |
+| `on_disk` | For enabling persistent storage | `False` |

docs/components/vectordbs/dbs/redis.mdx

@@ -0,0 +1,44 @@
+[Redis](https://redis.io/) is a scalable, real-time database that can store, search, and analyze vector data.
+
+### Installation
+```bash
+pip install redis redisvl
+```
+
+Redis Stack using Docker:
+```bash
+docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
+```
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "redis",
+        "config": {
+            "collection_name": "mem0",
+            "embedding_model_dims": 1536,
+            "redis_url": "redis://localhost:6379"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+### Config
+
+Let's see the available parameters for the `redis` config:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collection_name` | The name of the collection to store the vectors | `mem0` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `redis_url` | The URL of the Redis server | `None` |

docs/components/vectordbs/overview.mdx

@@ -0,0 +1,37 @@
+---
+title: Overview
+---
+
+Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.
+
+## Supported Vector Databases
+
+See the list of supported vector databases below.
+
+<CardGroup cols={3}>
+  <Card title="Qdrant" href="/components/vectordbs/dbs/qdrant"></Card>
+  <Card title="Chroma" href="/components/vectordbs/dbs/chroma"></Card>
+  <Card title="Pgvector" href="/components/vectordbs/dbs/pgvector"></Card>
+  <Card title="Milvus" href="/components/vectordbs/dbs/milvus"></Card>
+  <Card title="Azure AI Search" href="/components/vectordbs/dbs/azure_ai_search"></Card>
+  <Card title="Redis" href="/components/vectordbs/dbs/redis"></Card>
+  <Card title="Elasticsearch" href="/components/vectordbs/dbs/elasticsearch"></Card>
+</CardGroup>
+
+## Usage
+
+To utilize a vector database, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and `Qdrant` will be used as the vector database.
+
+For a comprehensive list of available parameters for vector database configuration, please refer to [Config](./config).
+
+## Common issues
+
+### Using model with different dimensions
+
+If you are using customized model, which is having different dimensions other than 1536
+for example 768, you may encounter below error:
+
+`ValueError: shapes (0,1536) and (768,) not aligned: 1536 (dim 1) != 768 (dim 0)`
+
+you could add `"embedding_model_dims": 768,` to the config of the vector_store to overcome this issue.
+

docs/contribution/guidelines.mdx

@@ -1,4 +0,0 @@
----
-title: '📋 Guidelines'
-url: https://github.com/embedchain/embedchain/blob/main/CONTRIBUTING.md
----

docs/contribution/javascript.mdx

@@ -1,4 +0,0 @@
----
-title: ' 🟨 Javascript'
-url: https://github.com/embedchain/embedchain/tree/main/embedchain-js
----

docs/deployment/embedchain_ai.mdx

@@ -1,17 +0,0 @@
----
-title: 'Embedchain.ai'
-description: 'Deploy your RAG application to embedchain.ai platform'
----
-
-## Deploy on Embedchain Platform
-
-Embedchain enables developers to deploy their LLM-powered apps in production using the Embedchain platform. The platform offers free access to context on your data through its REST API. Once the pipeline is deployed, you can update your data sources anytime after deployment.
-
-Deployment to Embedchain Platform is currently available on an invitation-only basis. To request access, please submit your information via the provided [Google Form](https://forms.gle/vigN11h7b4Ywat668). We will review your request and respond promptly.
-
-
-## Seeking help?
-
-If you run into issues with deployment, please feel free to reach out to us via any of the following methods:
-
-<Snippet file="get-help.mdx" />

docs/examples/ai_companion.mdx

@@ -0,0 +1,166 @@
+---
+title: AI Companion
+---
+
+You can create a personalised AI Companion using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
+
+## Overview
+
+The Personalized AI Companion leverages Mem0 to retain information across interactions, enabling a tailored learning experience. It creates separate memories for both the user and the companion. By integrating with OpenAI's GPT-4 model, the companion can provide detailed and context-aware responses to user queries.
+
+## Setup
+Before you begin, ensure you have the required dependencies installed. You can install the necessary packages using pip:
+
+```bash
+pip install openai mem0ai
+```
+
+## Full Code Example
+
+Below is the complete code to create and interact with an AI Companion using Mem0:
+
+```python
+from openai import OpenAI
+from mem0 import Memory
+import os
+
+# Set the OpenAI API key
+os.environ['OPENAI_API_KEY'] = 'sk-xxx'
+
+# Initialize the OpenAI client
+client = OpenAI()
+
+class Companion:
+    def __init__(self, user_id, companion_id):
+        """
+        Initialize the Companion with memory configuration, OpenAI client, and user IDs.
+        :param user_id: ID for storing user-related memories
+        :param companion_id: ID for storing companion-related memories
+        """
+        config = {
+            "vector_store": {
+                "provider": "qdrant",
+                "config": {
+                    "host": "localhost",
+                    "port": 6333,
+                }
+            },
+        }
+        self.memory = Memory.from_config(config)
+        self.client = client
+        self.app_id = "app-1"
+        self.USER_ID = user_id
+        self.companion_id = companion_id
+
+    def analyze_question(self, question):
+        """
+        Analyze the question to determine whether it's about the user or the companion.
+        """
+        check_prompt = f"""
+        Analyze the given input and determine whether the user is primarily:
+        1) Talking about themselves or asking for personal advice. They may use words like "I" for this. 
+        2) Inquiring about the AI companions's capabilities or characteristics They may use words like "you" for this. 
+
+        Respond with a single word:
+        - 'user' if the input is focused on the user
+        - 'companion' if the input is focused on the AI companion
+
+        If the input is ambiguous or doesn't clearly fit either category, respond with 'user'.
+
+        Input: {question}
+        """
+        response = self.client.chat.completions.create(
+            model="gpt-4",
+            messages=[{"role": "user", "content": check_prompt}]
+        )
+        return response.choices[0].message.content
+
+    def ask(self, question):
+        """
+        Ask a question to the AI and store the relevant facts in memory
+        :param question: The question to ask the AI.
+        """
+        check_answer = self.analyze_question(question)
+        user_id_to_use = self.USER_ID if check_answer == "user" else self.companion_id
+
+        previous_memories = self.memory.search(question, user_id=user_id_to_use)
+        relevant_memories_text = ""
+        if previous_memories:
+            relevant_memories_text = '\n'.join(mem["memory"] for mem in previous_memories)
+
+        prompt = f"User input: {question}\nPrevious {check_answer} memories: {relevant_memories_text}"
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are the user's romantic companion. Use the user's input and previous memories to respond. Answer based on the context provided."
+            },
+            {
+                "role": "user",
+                "content": prompt
+            }
+        ]
+
+        stream = self.client.chat.completions.create(
+            model="gpt-4",
+            stream=True,
+            messages=messages
+        )
+
+        answer = ""
+        for chunk in stream:
+            if chunk.choices[0].delta.content is not None:
+                content = chunk.choices[0].delta.content
+                print(content, end="")
+                answer += content
+        # Store the question and answer in memory
+        self.memory.add(question, user_id=self.USER_ID, metadata={"app_id": self.app_id})
+        self.memory.add(answer, user_id=self.companion_id, metadata={"app_id": self.app_id})
+
+    def get_memories(self, user_id=None):
+        """
+        Retrieve all memories associated with the given user ID.
+        :param user_id: Optional user ID to filter memories.
+        :return: List of memories.
+        """
+        return self.memory.get_all(user_id=user_id)
+
+# Example usage:
+user_id = "user"
+companion_id = "companion"
+ai_companion = Companion(user_id, companion_id)
+
+# Ask a question
+ai_companion.ask("Ive been missing you. What have you been up to off late?")
+```
+
+### Fetching Memories
+
+You can fetch all the memories at any point in time using the following code:
+
+```python
+def print_memories(user_id, label):
+    print(f"\n{label} Memories:")
+    memories = ai_companion.get_memories(user_id=user_id)
+    if memories:
+        for m in memories:
+            print(f"- {m['text']}")
+    else:
+        print("No memories found.")
+
+# Print user memories
+print_memories(user_id, "User")
+
+# Print companion memories
+print_memories(companion_id, "Companion")
+```
+
+### Key Points
+
+- **Initialization**: The Companion class is initialized with the necessary memory configuration and OpenAI client setup.
+- **Asking Questions**: The ask method sends a question to the AI and stores the relevant information in memory.
+- **Retrieving Memories**: The get_memories method fetches all stored memories associated with a user.
+
+### Conclusion
+
+As the conversation progresses, Mem0's memory automatically updates based on the interactions, providing a continuously improving personalized experience. This setup ensures that the AI Companion can offer contextually relevant and accurate responses, enhancing the user's experience.

docs/examples/customer-support-agent.mdx

@@ -0,0 +1,109 @@
+---
+title: Customer Support AI Agent
+---
+
+You can create a personalized Customer Support AI Agent using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
+
+## Overview
+
+The Customer Support AI Agent leverages Mem0 to retain information across interactions, enabling a personalized and efficient support experience.
+
+## Setup
+
+Install the necessary packages using pip:
+
+```bash
+pip install openai mem0ai
+```
+
+## Full Code Example
+
+Below is the simplified code to create and interact with a Customer Support AI Agent using Mem0:
+
+```python
+from openai import OpenAI
+from mem0 import Memory
+
+# Set the OpenAI API key
+os.environ['OPENAI_API_KEY'] = 'sk-xxx'
+
+class CustomerSupportAIAgent:
+    def __init__(self):
+        """
+        Initialize the CustomerSupportAIAgent with memory configuration and OpenAI client.
+        """
+        config = {
+            "vector_store": {
+                "provider": "qdrant",
+                "config": {
+                    "host": "localhost",
+                    "port": 6333,
+                }
+            },
+        }
+        self.memory = Memory.from_config(config)
+        self.client = OpenAI()
+        self.app_id = "customer-support"
+
+    def handle_query(self, query, user_id=None):
+        """
+        Handle a customer query and store the relevant information in memory.
+
+        :param query: The customer query to handle.
+        :param user_id: Optional user ID to associate with the memory.
+        """
+        # Start a streaming chat completion request to the AI
+        stream = self.client.chat.completions.create(
+            model="gpt-4",
+            stream=True,
+            messages=[
+                {"role": "system", "content": "You are a customer support AI agent."},
+                {"role": "user", "content": query}
+            ]
+        )
+        # Store the query in memory
+        self.memory.add(query, user_id=user_id, metadata={"app_id": self.app_id})
+
+        # Print the response from the AI in real-time
+        for chunk in stream:
+            if chunk.choices[0].delta.content is not None:
+                print(chunk.choices[0].delta.content, end="")
+
+    def get_memories(self, user_id=None):
+        """
+        Retrieve all memories associated with the given customer ID.
+
+        :param user_id: Optional user ID to filter memories.
+        :return: List of memories.
+        """
+        return self.memory.get_all(user_id=user_id)
+
+# Instantiate the CustomerSupportAIAgent
+support_agent = CustomerSupportAIAgent()
+
+# Define a customer ID
+customer_id = "jane_doe"
+
+# Handle a customer query
+support_agent.handle_query("I need help with my recent order. It hasn't arrived yet.", user_id=customer_id)
+```
+
+### Fetching Memories
+
+You can fetch all the memories at any point in time using the following code:
+
+```python
+memories = support_agent.get_memories(user_id=customer_id)
+for m in memories:
+    print(m['text'])
+```
+
+### Key Points
+
+- **Initialization**: The CustomerSupportAIAgent class is initialized with the necessary memory configuration and OpenAI client setup.
+- **Handling Queries**: The handle_query method sends a query to the AI and stores the relevant information in memory.
+- **Retrieving Memories**: The get_memories method fetches all stored memories associated with a customer.
+
+### Conclusion
+
+As the conversation progresses, Mem0's memory automatically updates based on the interactions, providing a continuously improving personalized support experience.

docs/examples/llama-index-mem0.mdx

@@ -0,0 +1,172 @@
+---
+title: LlamaIndex ReAct Agent
+---
+Create a ReAct Agent with LlamaIndex which uses Mem0 as the memory store.
+
+### Overview
+A ReAct agent combines reasoning and action capabilities, making it versatile for tasks requiring both thought processes (reasoning) and interaction with tools or APIs (acting). Mem0 as memory enhances these capabilities by allowing the agent to store and retrieve contextual information from past interactions.
+
+### Setup
+```bash
+pip install llama-index-core llama-index-memory-mem0
+```
+
+Initialize the LLM.
+```python
+import os
+from llama_index.llms.openai import OpenAI
+
+os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"
+llm = OpenAI(model="gpt-4o")
+```
+
+Initialize the Mem0 client. You can find your API key [here](https://app.mem0.ai/dashboard/). Read about Mem0 [Open Source](https://docs.mem0.ai/open-source/quickstart).
+```python
+os.environ["MEM0_API_KEY"] = "<your-mem0-api-key>"
+
+from llama_index.memory.mem0 import Mem0Memory
+
+context = {"user_id": "david"}
+memory_from_client = Mem0Memory.from_client(
+    context=context,
+    api_key=os.environ["MEM0_API_KEY"],
+    search_msg_limit=4,  # optional, default is 5
+)
+```
+
+Create the tools. These tools will be used by the agent to perform actions.
+```python
+from llama_index.core.tools import FunctionTool
+
+def call_fn(name: str):
+    """Call the provided name.
+    Args:
+        name: str (Name of the person)
+    """
+    return f"Calling... {name}"
+
+def email_fn(name: str):
+    """Email the provided name.
+    Args:
+        name: str (Name of the person)
+    """
+    return f"Emailing... {name}"
+
+def order_food(name: str, dish: str):
+    """Order food for the provided name.
+    Args:
+        name: str (Name of the person)
+        dish: str (Name of the dish)
+    """
+    return f"Ordering {dish} for {name}"
+
+call_tool = FunctionTool.from_defaults(fn=call_fn)
+email_tool = FunctionTool.from_defaults(fn=email_fn)
+order_food_tool = FunctionTool.from_defaults(fn=order_food)
+```
+
+Initialize the agent with tools and memory.
+```python
+from llama_index.core.agent import FunctionCallingAgent
+
+agent = FunctionCallingAgent.from_tools(
+    [call_tool, email_tool, order_food_tool],
+    llm=llm,
+    memory=memory_from_client,  # or memory_from_config
+    verbose=True,
+)
+```
+
+Start the chat.
+<Note> The agent will use the Mem0 to store the relavant memories from the chat. </Note>
+
+Input
+```python
+response = agent.chat("Hi, My name is David")
+print(response)
+```
+Output
+```text
+> Running step bf44a75a-a920-4cf3-944e-b6e6b5695043. Step input: Hi, My name is David
+Added user message to memory: Hi, My name is David
+=== LLM Response ===
+Hello, David! How can I assist you today?
+```
+
+Input
+```python
+response = agent.chat("I love to eat pizza on weekends")
+print(response)
+```
+Output
+```text
+> Running step 845783b0-b85b-487c-baee-8460ebe8b38d. Step input: I love to eat pizza on weekends
+Added user message to memory: I love to eat pizza on weekends
+=== LLM Response ===
+Pizza is a great choice for the weekend! If you'd like, I can help you order some. Just let me know what kind of pizza you prefer!
+```
+Input
+```python
+response = agent.chat("My preferred way of communication is email")
+print(response)
+```
+Output
+```text
+> Running step 345842f0-f8a0-42ea-a1b7-612265d72a92. Step input: My preferred way of communication is email
+Added user message to memory: My preferred way of communication is email
+=== LLM Response ===
+Got it! If you need any assistance or have any requests, feel free to let me know, and I can communicate with you via email.
+```
+
+### Using the agent WITHOUT memory
+Input
+```python
+agent = FunctionCallingAgent.from_tools(
+    [call_tool, email_tool, order_food_tool],
+    # memory is not provided
+    llm=llm,
+    verbose=True,
+)
+response = agent.chat("I am feeling hungry, order me something and send me the bill")
+print(response)
+```
+Output
+```text
+> Running step e89eb75d-75e1-4dea-a8c8-5c3d4b77882d. Step input: I am feeling hungry, order me something and send me the bill
+Added user message to memory: I am feeling hungry, order me something and send me the bill
+=== LLM Response ===
+Please let me know your name and the dish you'd like to order, and I'll take care of it for you!
+```
+<Note> The agent is not able to remember the past prefernces that user shared in previous chats. </Note>
+
+### Using the agent WITH memory
+Input
+```python
+agent = FunctionCallingAgent.from_tools(
+    [call_tool, email_tool, order_food_tool],
+    llm=llm,
+    # memory is provided
+    memory=memory_from_client,  # or memory_from_config
+    verbose=True,
+)
+response = agent.chat("I am feeling hungry, order me something and send me the bill")
+print(response)
+```
+
+Output
+```text
+> Running step 5e473db9-3973-4cb1-a5fd-860be0ab0006. Step input: I am feeling hungry, order me something and send me the bill
+Added user message to memory: I am feeling hungry, order me something and send me the bill
+=== Calling Function ===
+Calling function: order_food with args: {"name": "David", "dish": "pizza"}
+=== Function Output ===
+Ordering pizza for David
+=== Calling Function ===
+Calling function: email_fn with args: {"name": "David"}
+=== Function Output ===
+Emailing... David
+> Running step 38080544-6b37-4bb2-aab2-7670100d926e. Step input: None
+=== LLM Response ===
+I've ordered a pizza for you, and the bill has been sent to your email. Enjoy your meal! If there's anything else you need, feel free to let me know.
+```
+<Note> The agent is able to remember the past prefernces that user shared and use them to perform actions. </Note>

docs/examples/mem0-with-ollama.mdx

@@ -0,0 +1,73 @@
+---
+title: Mem0 with Ollama
+---
+
+## Running Mem0 Locally with Ollama
+
+Mem0 can be utilized entirely locally by leveraging Ollama for both the embedding model and the language model (LLM). This guide will walk you through the necessary steps and provide the complete code to get you started.
+
+### Overview
+
+By using Ollama, you can run Mem0 locally, which allows for greater control over your data and models. This setup uses Ollama for both the embedding model and the language model, providing a fully local solution.
+
+### Setup
+
+Before you begin, ensure you have Mem0 and Ollama installed and properly configured on your local machine.
+
+### Full Code Example
+
+Below is the complete code to set up and use Mem0 locally with Ollama:
+
+```python
+import os
+from mem0 import Memory
+
+config = {
+    "vector_store": {
+        "provider": "qdrant",
+        "config": {
+            "collection_name": "test",
+            "host": "localhost",
+            "port": 6333,
+            "embedding_model_dims": 768,  # Change this according to your local model's dimensions
+        },
+    },
+    "llm": {
+        "provider": "ollama",
+        "config": {
+            "model": "llama3.1:latest",
+            "temperature": 0,
+            "max_tokens": 8000,
+            "ollama_base_url": "http://localhost:11434",  # Ensure this URL is correct
+        },
+    },
+    "embedder": {
+        "provider": "ollama",
+        "config": {
+            "model": "nomic-embed-text:latest",
+            # Alternatively, you can use "snowflake-arctic-embed:latest"
+            "ollama_base_url": "http://localhost:11434",
+        },
+    },
+}
+
+# Initialize Memory with the configuration
+m = Memory.from_config(config)
+
+# Add a memory
+m.add("I'm visiting Paris", user_id="john")
+
+# Retrieve memories
+memories = m.get_all(user_id="john")
+```
+
+### Key Points
+
+- **Configuration**: The setup involves configuring the vector store, language model, and embedding model to use local resources.
+- **Vector Store**: Qdrant is used as the vector store, running on localhost.
+- **Language Model**: Ollama is used as the LLM provider, with the "llama3.1:latest" model.
+- **Embedding Model**: Ollama is also used for embeddings, with the "nomic-embed-text:latest" model.
+
+### Conclusion
+
+This local setup of Mem0 using Ollama provides a fully self-contained solution for memory management and AI interactions. It allows for greater control over your data and models while still leveraging the powerful capabilities of Mem0.

docs/examples/overview.mdx

@@ -0,0 +1,35 @@
+---
+title: Overview
+description: How to use mem0 in your existing applications?
+---
+
+
+With Mem0, you can create stateful LLM-based applications such as chatbots, virtual assistants, or AI agents. Mem0 enhances your applications by providing a memory layer that makes responses:
+
+- More personalized
+- More reliable
+- Cost-effective by reducing the number of LLM interactions
+- More engaging
+- Enables long-term memory
+
+Here are some examples of how Mem0 can be integrated into various applications:
+
+## Examples
+
+<CardGroup cols={2}>
+  <Card title="Mem0 with Ollama" icon="square-1" href="/examples/mem0-with-ollama">
+    Run Mem0 locally with Ollama.
+  </Card>
+  <Card title="Personal AI Tutor" icon="square-2" href="/examples/personal-ai-tutor">
+    Create a Personalized AI Tutor that adapts to student progress and learning preferences.
+  </Card>
+  <Card title="Personal Travel Assistant" icon="square-3" href="/examples/personal-travel-assistant">
+    Build a Personalized AI Travel Assistant that understands your travel preferences and past itineraries.
+  </Card>
+  <Card title="Customer Support Agent" icon="square-4" href="/examples/customer-support-agent">
+    Develop a Personal AI Assistant that remembers user preferences, past interactions, and context to provide personalized and efficient assistance.
+  </Card>
+  <Card title="LlamaIndex Mem0" icon="square-5" href="/examples/llama-index-mem0">
+    Create a ReAct Agent with LlamaIndex which uses Mem0 as the memory store.
+  </Card>
+</CardGroup>

docs/examples/personal-ai-tutor.mdx

@@ -0,0 +1,111 @@
+---
+title: Personalized AI Tutor
+---
+
+You can create a personalized AI Tutor using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
+
+## Overview
+
+The Personalized AI Tutor leverages Mem0 to retain information across interactions, enabling a tailored learning experience. By integrating with OpenAI's GPT-4 model, the tutor can provide detailed and context-aware responses to user queries.
+
+## Setup
+Before you begin, ensure you have the required dependencies installed. You can install the necessary packages using pip:
+
+```bash
+pip install openai mem0ai
+```
+
+## Full Code Example
+
+Below is the complete code to create and interact with a Personalized AI Tutor using Mem0:
+
+```python
+from openai import OpenAI
+from mem0 import Memory
+
+# Set the OpenAI API key
+os.environ['OPENAI_API_KEY'] = 'sk-xxx'
+
+# Initialize the OpenAI client
+client = OpenAI()
+
+class PersonalAITutor:
+    def __init__(self):
+        """
+        Initialize the PersonalAITutor with memory configuration and OpenAI client.
+        """
+        config = {
+            "vector_store": {
+                "provider": "qdrant",
+                "config": {
+                    "host": "localhost",
+                    "port": 6333,
+                }
+            },
+        }
+        self.memory = Memory.from_config(config)
+        self.client = client
+        self.app_id = "app-1"
+
+    def ask(self, question, user_id=None):
+        """
+        Ask a question to the AI and store the relevant facts in memory
+
+        :param question: The question to ask the AI.
+        :param user_id: Optional user ID to associate with the memory.
+        """
+        # Start a streaming chat completion request to the AI
+        stream = self.client.chat.completions.create(
+            model="gpt-4",
+            stream=True,
+            messages=[
+                {"role": "system", "content": "You are a personal AI Tutor."},
+                {"role": "user", "content": question}
+            ]
+        )
+        # Store the question in memory
+        self.memory.add(question, user_id=user_id, metadata={"app_id": self.app_id})
+
+        # Print the response from the AI in real-time
+        for chunk in stream:
+            if chunk.choices[0].delta.content is not None:
+                print(chunk.choices[0].delta.content, end="")
+
+    def get_memories(self, user_id=None):
+        """
+        Retrieve all memories associated with the given user ID.
+
+        :param user_id: Optional user ID to filter memories.
+        :return: List of memories.
+        """
+        return self.memory.get_all(user_id=user_id)
+
+# Instantiate the PersonalAITutor
+ai_tutor = PersonalAITutor()
+
+# Define a user ID
+user_id = "john_doe"
+
+# Ask a question
+ai_tutor.ask("I am learning introduction to CS. What is queue? Briefly explain.", user_id=user_id)
+```
+
+### Fetching Memories
+
+You can fetch all the memories at any point in time using the following code:
+
+```python
+memories = ai_tutor.get_memories(user_id=user_id)
+for m in memories:
+    print(m['text'])
+```
+
+### Key Points
+
+- **Initialization**: The PersonalAITutor class is initialized with the necessary memory configuration and OpenAI client setup.
+- **Asking Questions**: The ask method sends a question to the AI and stores the relevant information in memory.
+- **Retrieving Memories**: The get_memories method fetches all stored memories associated with a user.
+
+### Conclusion
+
+As the conversation progresses, Mem0's memory automatically updates based on the interactions, providing a continuously improving personalized learning experience. This setup ensures that the AI Tutor can offer contextually relevant and accurate responses, enhancing the overall educational process.

docs/examples/personal-travel-assistant.mdx

@@ -0,0 +1,195 @@
+---
+title: Personal AI Travel Assistant
+---
+Create a personalized AI Travel Assistant using Mem0. This guide provides step-by-step instructions and the complete code to get you started.
+
+## Overview
+
+The Personalized AI Travel Assistant uses Mem0 to store and retrieve information across interactions, enabling a tailored travel planning experience. It integrates with OpenAI's GPT-4 model to provide detailed and context-aware responses to user queries.
+
+## Setup
+
+Install the required dependencies using pip:
+
+```bash
+pip install openai mem0ai
+```
+
+## Full Code Example
+
+Here's the complete code to create and interact with a Personalized AI Travel Assistant using Mem0:
+
+<CodeGroup>
+
+```python After v1.1
+import os
+from openai import OpenAI
+from mem0 import Memory
+
+# Set the OpenAI API key
+os.environ['OPENAI_API_KEY'] = "sk-xxx"
+
+config = {
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "model": "gpt-4o",
+            "temperature": 0.1,
+            "max_tokens": 2000,
+        }
+    },
+    "embedder": {
+        "provider": "openai",
+        "config": {
+            "model": "text-embedding-3-large"
+        }
+    },
+    "vector_store": {
+        "provider": "qdrant",
+        "config": {
+            "collection_name": "test",
+            "embedding_model_dims": 3072,
+        }
+    },
+    "version": "v1.1",
+}
+
+class PersonalTravelAssistant:
+    def __init__(self):
+        self.client = OpenAI()
+        self.memory = Memory.from_config(config)
+        self.messages = [{"role": "system", "content": "You are a personal AI Assistant."}]
+
+    def ask_question(self, question, user_id):
+        # Fetch previous related memories
+        previous_memories = self.search_memories(question, user_id=user_id)
+        prompt = question
+        if previous_memories:
+            prompt = f"User input: {question}\n Previous memories: {previous_memories}"
+        self.messages.append({"role": "user", "content": prompt})
+
+        # Generate response using GPT-4o
+        response = self.client.chat.completions.create(
+            model="gpt-4o",
+            messages=self.messages
+        )
+        answer = response.choices[0].message.content
+        self.messages.append({"role": "assistant", "content": answer})
+
+        # Store the question in memory
+        self.memory.add(question, user_id=user_id)
+        return answer
+
+    def get_memories(self, user_id):
+        memories = self.memory.get_all(user_id=user_id)
+        return [m['memory'] for m in memories['memories']]
+
+    def search_memories(self, query, user_id):
+        memories = self.memory.search(query, user_id=user_id)
+        return [m['memory'] for m in memories['memories']]
+
+# Usage example
+user_id = "traveler_123"
+ai_assistant = PersonalTravelAssistant()
+
+def main():
+    while True:
+        question = input("Question: ")
+        if question.lower() in ['q', 'exit']:
+            print("Exiting...")
+            break
+
+        answer = ai_assistant.ask_question(question, user_id=user_id)
+        print(f"Answer: {answer}")
+        memories = ai_assistant.get_memories(user_id=user_id)
+        print("Memories:")
+        for memory in memories:
+            print(f"- {memory}")
+        print("-----")
+
+if __name__ == "__main__":
+    main()
+```
+
+```python Before v1.1
+import os
+from openai import OpenAI
+from mem0 import Memory
+
+# Set the OpenAI API key
+os.environ['OPENAI_API_KEY'] = 'sk-xxx'
+
+class PersonalTravelAssistant:
+    def __init__(self):
+        self.client = OpenAI()
+        self.memory = Memory()
+        self.messages = [{"role": "system", "content": "You are a personal AI Assistant."}]
+
+    def ask_question(self, question, user_id):
+        # Fetch previous related memories
+        previous_memories = self.search_memories(question, user_id=user_id)
+        prompt = question
+        if previous_memories:
+            prompt = f"User input: {question}\n Previous memories: {previous_memories}"
+        self.messages.append({"role": "user", "content": prompt})
+
+        # Generate response using GPT-4o
+        response = self.client.chat.completions.create(
+            model="gpt-4o",
+            messages=self.messages
+        )
+        answer = response.choices[0].message.content
+        self.messages.append({"role": "assistant", "content": answer})
+
+        # Store the question in memory
+        self.memory.add(question, user_id=user_id)
+        return answer
+
+    def get_memories(self, user_id):
+        memories = self.memory.get_all(user_id=user_id)
+        return [m['memory'] for m in memories['memories']]
+
+    def search_memories(self, query, user_id):
+        memories = self.memory.search(query, user_id=user_id)
+        return [m['memory'] for m in memories['memories']]
+
+# Usage example
+user_id = "traveler_123"
+ai_assistant = PersonalTravelAssistant()
+
+def main():
+    while True:
+        question = input("Question: ")
+        if question.lower() in ['q', 'exit']:
+            print("Exiting...")
+            break
+
+        answer = ai_assistant.ask_question(question, user_id=user_id)
+        print(f"Answer: {answer}")
+        memories = ai_assistant.get_memories(user_id=user_id)
+        print("Memories:")
+        for memory in memories:
+            print(f"- {memory}")
+        print("-----")
+
+if __name__ == "__main__":
+    main()
+```
+</CodeGroup>
+
+
+## Key Components
+
+- **Initialization**: The `PersonalTravelAssistant` class is initialized with the OpenAI client and Mem0 memory setup.
+- **Asking Questions**: The `ask_question` method sends a question to the AI, incorporates previous memories, and stores new information.
+- **Memory Management**: The `get_memories` and search_memories methods handle retrieval and searching of stored memories.
+
+## Usage
+
+1. Set your OpenAI API key in the environment variable.
+2. Instantiate the `PersonalTravelAssistant`.
+3. Use the `main()` function to interact with the assistant in a loop.
+
+## Conclusion
+
+This Personalized AI Travel Assistant leverages Mem0's memory capabilities to provide context-aware responses. As you interact with it, the assistant learns and improves, offering increasingly personalized travel advice and information.

docs/favicon.svg

@@ -0,0 +1,49 @@
+<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M7.95343 21.1394C4.89586 21.1304 2.25471 19.458 0.987296 16.2895C-0.280118 13.121 0.108924 9.16314 1.74363 5.61505C4.8012 5.62409 7.44235 7.29648 8.70976 10.465C9.97718 13.6335 9.58814 17.5914 7.95343 21.1394Z" fill="white"/>
+<path d="M7.95343 21.1394C4.89586 21.1304 2.25471 19.458 0.987296 16.2895C-0.280118 13.121 0.108924 9.16314 1.74363 5.61505C4.8012 5.62409 7.44235 7.29648 8.70976 10.465C9.97718 13.6335 9.58814 17.5914 7.95343 21.1394Z" fill="url(#paint0_radial_101_2703)"/>
+<path d="M7.95343 21.1394C4.89586 21.1304 2.25471 19.458 0.987296 16.2895C-0.280118 13.121 0.108924 9.16314 1.74363 5.61505C4.8012 5.62409 7.44235 7.29648 8.70976 10.465C9.97718 13.6335 9.58814 17.5914 7.95343 21.1394Z" fill="black" fill-opacity="0.5" style="mix-blend-mode:hard-light"/>
+<path d="M7.95343 21.1394C4.89586 21.1304 2.25471 19.458 0.987296 16.2895C-0.280118 13.121 0.108924 9.16314 1.74363 5.61505C4.8012 5.62409 7.44235 7.29648 8.70976 10.465C9.97718 13.6335 9.58814 17.5914 7.95343 21.1394Z" fill="url(#paint1_linear_101_2703)" fill-opacity="0.5" style="mix-blend-mode:hard-light"/>
+<path d="M8.68359 10.4755C9.94543 13.63 9.56145 17.5723 7.9354 21.1112C4.89702 21.0957 2.27411 19.4306 1.01347 16.279C-0.248375 13.1245 0.135612 9.18218 1.76165 5.64328C4.80004 5.65883 7.42295 7.32386 8.68359 10.4755Z" stroke="url(#paint2_linear_101_2703)" stroke-opacity="0.05" stroke-width="0.056338"/>
+<path d="M7.31038 21.2574C11.3543 20.2215 14.8836 17.3754 16.6285 13.2361C18.3735 9.09671 17.9448 4.58749 15.8598 0.976291C11.8159 2.01214 8.2866 4.85826 6.54167 8.99762C4.79674 13.137 5.2254 17.6462 7.31038 21.2574Z" fill="white"/>
+<path d="M7.31038 21.2574C11.3543 20.2215 14.8836 17.3754 16.6285 13.2361C18.3735 9.09671 17.9448 4.58749 15.8598 0.976291C11.8159 2.01214 8.2866 4.85826 6.54167 8.99762C4.79674 13.137 5.2254 17.6462 7.31038 21.2574Z" fill="url(#paint3_radial_101_2703)"/>
+<path d="M16.6026 13.2251C14.8642 17.349 11.3512 20.1866 7.32411 21.2248C5.25257 17.624 4.82926 13.1324 6.56764 9.00855C8.30603 4.88472 11.819 2.04706 15.8461 1.00889C17.9176 4.60967 18.3409 9.10131 16.6026 13.2251Z" stroke="url(#paint4_linear_101_2703)" stroke-opacity="0.05" stroke-width="0.056338"/>
+<path d="M7.23368 21.2069C9.78906 23.2373 13.2102 23.9506 16.5772 22.8141C19.9441 21.6775 22.5058 18.9445 23.7304 15.6382C21.175 13.6078 17.7538 12.8944 14.3869 14.031C11.0199 15.1676 8.45822 17.9006 7.23368 21.2069Z" fill="white"/>
+<path d="M7.23368 21.2069C9.78906 23.2373 13.2102 23.9506 16.5772 22.8141C19.9441 21.6775 22.5058 18.9445 23.7304 15.6382C21.175 13.6078 17.7538 12.8944 14.3869 14.031C11.0199 15.1676 8.45822 17.9006 7.23368 21.2069Z" fill="url(#paint5_radial_101_2703)"/>
+<path d="M7.23368 21.2069C9.78906 23.2373 13.2102 23.9506 16.5772 22.8141C19.9441 21.6775 22.5058 18.9445 23.7304 15.6382C21.175 13.6078 17.7538 12.8944 14.3869 14.031C11.0199 15.1676 8.45822 17.9006 7.23368 21.2069Z" fill="black" fill-opacity="0.2" style="mix-blend-mode:hard-light"/>
+<path d="M7.23368 21.2069C9.78906 23.2373 13.2102 23.9506 16.5772 22.8141C19.9441 21.6775 22.5058 18.9445 23.7304 15.6382C21.175 13.6078 17.7538 12.8944 14.3869 14.031C11.0199 15.1676 8.45822 17.9006 7.23368 21.2069Z" fill="url(#paint6_linear_101_2703)" fill-opacity="0.5" style="mix-blend-mode:hard-light"/>
+<path d="M16.5682 22.7874C13.2176 23.9184 9.81361 23.2124 7.2672 21.1975C8.49194 17.9068 11.0444 15.189 14.3959 14.0577C17.7465 12.9266 21.1504 13.6326 23.6968 15.6476C22.4721 18.9383 19.9196 21.656 16.5682 22.7874Z" stroke="url(#paint7_linear_101_2703)" stroke-opacity="0.05" stroke-width="0.056338"/>
+<defs>
+<radialGradient id="paint0_radial_101_2703" cx="0" cy="0" r="1" gradientUnits="userSpaceOnUse" gradientTransform="translate(-3.00503 15.023) rotate(-10.029) scale(17.9572 17.784)">
+<stop stop-color="#00B0BB"/>
+<stop offset="1" stop-color="#00DB65"/>
+</radialGradient>
+<linearGradient id="paint1_linear_101_2703" x1="7.39036" y1="4.81308" x2="1.62975" y2="18.6894" gradientUnits="userSpaceOnUse">
+<stop stop-color="#18E299"/>
+<stop offset="1"/>
+</linearGradient>
+<linearGradient id="paint2_linear_101_2703" x1="7.94816" y1="8.01563" x2="1.7612" y2="18.746" gradientUnits="userSpaceOnUse">
+<stop/>
+<stop offset="1" stop-opacity="0"/>
+</linearGradient>
+<radialGradient id="paint3_radial_101_2703" cx="0" cy="0" r="1" gradientUnits="userSpaceOnUse" gradientTransform="translate(8.11404 20.8822) rotate(-75.7542) scale(21.6246 23.7772)">
+<stop stop-color="#00BBBB"/>
+<stop offset="0.712616" stop-color="#00DB65"/>
+</radialGradient>
+<linearGradient id="paint4_linear_101_2703" x1="7.60205" y1="5.8709" x2="15.5561" y2="16.3719" gradientUnits="userSpaceOnUse">
+<stop/>
+<stop offset="1" stop-opacity="0"/>
+</linearGradient>
+<radialGradient id="paint5_radial_101_2703" cx="0" cy="0" r="1" gradientUnits="userSpaceOnUse" gradientTransform="translate(7.84537 21.5181) rotate(-20.3525) scale(18.5603 17.32)">
+<stop stop-color="#00B0BB"/>
+<stop offset="1" stop-color="#00DB65"/>
+</radialGradient>
+<linearGradient id="paint6_linear_101_2703" x1="16.8078" y1="13.0071" x2="10.0409" y2="22.9937" gradientUnits="userSpaceOnUse">
+<stop stop-color="#00B1BC"/>
+<stop offset="1"/>
+</linearGradient>
+<linearGradient id="paint7_linear_101_2703" x1="16.8078" y1="13.0071" x2="14.1687" y2="23.841" gradientUnits="userSpaceOnUse">
+<stop/>
+<stop offset="1" stop-opacity="0"/>
+</linearGradient>
+</defs>
+</svg>

docs/features.mdx

@@ -0,0 +1,57 @@
+---
+title: Features
+---
+
+## Core features
+
+- **User, Session, and AI Agent Memory**: Retains information across sessions and interactions for users and AI agents, ensuring continuity and context.
+- **Adaptive Personalization**: Continuously updates memories based on user interactions and feedback.
+- **Developer-Friendly API**: Offers a straightforward API for seamless integration into various applications.
+- **Platform Consistency**: Ensures consistent behavior and data across different platforms and devices.
+- **Managed Service**: Provides a hosted solution for easy deployment and maintenance.
+- **Save Costs**: Saves costs by adding relevent memories instead of complete transcripts to context window
+
+
+
+## How does Mem0 work?
+
+Mem0 leverages a hybrid database approach to manage and retrieve long-term memories for AI agents and assistants. Each memory is associated with a unique identifier, such as a user ID or agent ID, allowing Mem0 to organize and access memories specific to an individual or context.
+
+When a message is added to the Mem0 using add()  method, the system extracts relevant facts and preferences and stores it across data stores: a vector database, a key-value database, and a graph database. This hybrid approach ensures that different types of information are stored in the most efficient manner, making subsequent searches quick and effective.
+
+When an AI agent or LLM needs to recall memories, it uses the search() method. Mem0 then performs search across these data stores, retrieving relevant information from each source. This information is then passed through a scoring layer, which evaluates their importance based on relevance, importance, and recency. This ensures that only the most personalized and useful context is surfaced.
+
+The retrieved memories can then be appended to the LLM's prompt as needed, making responses personalized and relevant.
+
+
+## Common Use Cases
+
+- **Personalized Learning Assistants**: Long-term memory allows learning assistants to remember user preferences, strengths and weaknesses, and progress, providing a more tailored and effective learning experience.
+
+- **Customer Support AI Agents**: By retaining information from previous interactions, customer support bots can offer more accurate and context-aware assistance, improving customer satisfaction and reducing resolution times.
+
+- **Healthcare Assistants**: Long-term memory enables healthcare assistants to keep track of patient history, medication schedules, and treatment plans, ensuring personalized and consistent care.
+
+- **Virtual Companions**: Virtual companions can use long-term memory to build deeper relationships with users by remembering personal details, preferences, and past conversations, making interactions more delightful.
+
+- **Productivity Tools**: Long-term memory helps productivity tools remember user habits, frequently used documents, and task history, streamlining workflows and enhancing efficiency.
+
+- **Gaming AI**: In gaming, AI with long-term memory can create more immersive experiences by remembering player choices, strategies, and progress, adapting the game environment accordingly.
+
+## How is Mem0 different from RAG?
+
+Mem0's memory implementation for Large Language Models (LLMs) offers several advantages over Retrieval-Augmented Generation (RAG):
+
+- **Entity Relationships**: Mem0 can understand and relate entities across different interactions, unlike RAG which retrieves information from static documents. This leads to a deeper understanding of context and relationships.
+
+- **Contextual Continuity**: Mem0 retains information across sessions, maintaining continuity in conversations and interactions, which is essential for long-term engagement applications like virtual companions or personalized learning assistants.
+
+- **Adaptive Learning**: Mem0 improves its personalization based on user interactions and feedback, making the memory more accurate and tailored to individual users over time.
+
+- **Dynamic Updates**: Mem0 can dynamically update its memory with new information and interactions, unlike RAG which relies on static data. This allows for real-time adjustments and improvements, enhancing the user experience.
+
+These advanced memory capabilities make Mem0 a powerful tool for developers aiming to create personalized and context-aware AI applications.
+
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx" />

docs/features/async-client.mdx

@@ -0,0 +1,172 @@
+---
+title: Async Client
+description: 'Asynchronous client for Mem0'
+---
+
+The `AsyncMemoryClient` is an asynchronous client for interacting with the Mem0 API. It provides similar functionality to the synchronous `MemoryClient` but allows for non-blocking operations, which can be beneficial in applications that require high concurrency.
+
+## Initialization
+
+To use the async client, you first need to initialize it:
+
+<CodeGroup>
+
+```python Python
+from mem0 import AsyncMemoryClient
+client = AsyncMemoryClient(api_key="your-api-key")
+```
+
+```javascript JavaScript
+const { MemoryClient } = require('mem0ai');
+const client = new MemoryClient('your-api-key');
+```
+
+</CodeGroup>
+
+## Methods
+
+The `AsyncMemoryClient` provides the following methods:
+
+### Add
+
+Add a new memory asynchronously.
+
+<CodeGroup>
+
+```python Python
+messages = [
+    {"role": "user", "content": "Alice loves playing badminton"},
+    {"role": "assistant", "content": "That's great! Alice is a fitness freak"},
+]
+await client.add(messages, user_id="alice")
+```
+
+```javascript JavaScript
+const messages = [
+    {"role": "user", "content": "Alice loves playing badminton"},
+    {"role": "assistant", "content": "That's great! Alice is a fitness freak"},
+];
+await client.add(messages, { user_id: "alice" });
+```
+
+</CodeGroup>
+
+### Search
+
+Search for memories based on a query asynchronously.
+
+<CodeGroup>
+
+```python Python
+await client.search(query="What is Alice's favorite sport?", user_id="alice")
+```
+
+```javascript JavaScript
+await client.search("What is Alice's favorite sport?", { user_id: "alice" });
+```
+
+</CodeGroup>
+
+### Get All
+
+Retrieve all memories for a user asynchronously.
+
+<CodeGroup>
+
+```python Python
+await client.get_all(user_id="alice")
+```
+
+```javascript JavaScript
+await client.getAll({ user_id: "alice" });
+```
+
+</CodeGroup>
+
+### Delete
+
+Delete a specific memory asynchronously.
+
+<CodeGroup>
+
+```python Python
+await client.delete(memory_id="memory-id-here")
+```
+
+```javascript JavaScript
+await client.delete("memory-id-here");
+```
+
+</CodeGroup>
+
+### Delete All
+
+Delete all memories for a user asynchronously.
+
+<CodeGroup>
+
+```python Python
+await client.delete_all(user_id="alice")
+```
+
+```javascript JavaScript
+await client.deleteAll({ user_id: "alice" });
+```
+
+</CodeGroup>
+
+### History
+
+Get the history of a specific memory asynchronously.
+
+<CodeGroup>
+
+```python Python
+await client.history(memory_id="memory-id-here")
+```
+
+```javascript JavaScript
+await client.history("memory-id-here");
+```
+
+</CodeGroup>
+
+### Users
+
+Get all users, agents, and runs which have memories associated with them asynchronously.
+
+<CodeGroup>
+
+```python Python
+await client.users()
+```
+
+```javascript JavaScript
+await client.users();
+```
+
+</CodeGroup>
+
+### Reset
+
+Reset the client, deleting all users and memories asynchronously.
+
+<CodeGroup>
+
+```python Python
+await client.reset()
+```
+
+```javascript JavaScript
+await client.reset();
+```
+
+</CodeGroup>
+
+## Conclusion
+
+The `AsyncMemoryClient` provides a powerful way to interact with the Mem0 API asynchronously, allowing for more efficient and responsive applications. By using this client, you can perform memory operations without blocking your application's execution.
+
+If you have any questions or need further assistance, please don't hesitate to reach out:
+
+<Snippet file="get-help.mdx" />

docs/features/custom-categories.mdx

@@ -0,0 +1,191 @@
+---
+title: Custom Categories
+description: 'Enhance your product experience by adding custom categories tailored to your needs'
+---
+
+## How to set custom categories?
+
+You can now create custom categories tailored to your specific needs, instead of using the default categories such as travel, sports, music, and more (see [default categories](#default-categories) below). **When custom categories are provided, they will override the default categories.**
+
+There are two ways to set custom categories:
+
+### 1. Project Level
+
+You can set custom categories at the project level, which will be applied to all memories added within that project. Mem0 will automatically assign relevant categories from your custom set to new memories based on their content. Setting custom categories at the project level will override the default categories.
+
+Here's how to set custom categories:
+
+<CodeGroup>
+```python Code
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="<your_mem0_api_key>")
+
+# Update custom categories
+new_categories = [
+    {"lifestyle_management_concerns": "Tracks daily routines, habits, hobbies and interests including cooking, time management and work-life balance"},
+    {"seeking_structure": "Documents goals around creating routines, schedules, and organized systems in various life areas"},
+    {"personal_information": "Basic information about the user including name, preferences, and personality traits"}
+]
+
+response = client.update_custom_instructions_and_categories({"custom_categories": new_categories})
+print(response)
+```
+
+```json Output
+{
+    "message": "Updated custom categories"
+}
+```
+</CodeGroup>
+
+This is how you will use these custom categories during the `add` API call:
+
+<CodeGroup>
+```python Code
+messages = [
+    {"role": "user", "content": "My name is Alice. I need help organizing my daily schedule better. I feel overwhelmed trying to balance work, exercise, and social life."},
+    {"role": "assistant", "content": "I understand how overwhelming that can feel. Let's break this down together. What specific areas of your schedule feel most challenging to manage?"},
+    {"role": "user", "content": "I want to be more productive at work, maintain a consistent workout routine, and still have energy for friends and hobbies."},
+    {"role": "assistant", "content": "Those are great goals for better time management. What's one small change you could make to start improving your daily routine?"},
+]
+
+# Add memories with custom categories
+client.add(messages, user_id="alice"))
+```
+
+```python Memories with categories
+# Following categories will be created for the memories added
+Wants to have energy for friends and hobbies (lifestyle_management_concerns)
+Wants to maintain a consistent workout routine (seeking_structure, lifestyle_management_concerns)
+Wants to be more productive at work (lifestyle_management_concerns, seeking_structure)
+Name is Alice (personal_information)
+```
+</CodeGroup>
+
+You can also retrieve the current custom categories:
+
+<CodeGroup>
+```python Code
+# Get current custom categories
+categories = client.get_project(fields=["custom_categories"])
+print(categories)
+```
+
+```json Output
+{
+  "custom_categories": [
+    {"lifestyle_management_concerns": "Tracks daily routines, habits, hobbies and interests including cooking, time management and work-life balance"},
+    {"seeking_structure": "Documents goals around creating routines, schedules, and organized systems in various life areas"},
+    {"personal_information": "Basic information about the user including name, preferences, and personality traits"}
+  ]
+}
+
+```
+</CodeGroup>
+
+These project-level categories will be automatically applied to all new memories added to the project.
+
+
+
+### 2. During the `add` API call
+You can also set custom categories during the `add` API call. This will override any project-level custom categories for that specific memory addition. For example, if you want to use different categories for food-related memories, you can provide custom categories like "food" and "user_preferences" in the `add` call. These custom categories will be used instead of the project-level categories when categorizing those specific memories.
+
+<CodeGroup>
+```python Code
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="<your_mem0_api_key>")
+
+custom_categories = [
+    {"seeking_structure": "Documents goals around creating routines, schedules, and organized systems in various life areas"},
+    {"personal_information": "Basic information about the user including name, preferences, and personality traits"}
+]
+
+messages = [
+    {"role": "user", "content": "My name is Alice. I need help organizing my daily schedule better. I feel overwhelmed trying to balance work, exercise, and social life."},
+    {"role": "assistant", "content": "I understand how overwhelming that can feel. Let's break this down together. What specific areas of your schedule feel most challenging to manage?"},
+    {"role": "user", "content": "I want to be more productive at work, maintain a consistent workout routine, and still have energy for friends and hobbies."},
+    {"role": "assistant", "content": "Those are great goals for better time management. What's one small change you could make to start improving your daily routine?"},
+]
+
+client.add(messages, user_id="alice", custom_categories=custom_categories)
+```
+
+```python Memories with categories
+# Following categories will be created for the memories added
+Wants to have energy for friends and hobbies (seeking_structure)
+Wants to maintain a consistent workout routine (seeking_structure)
+Wants to be more productive at work (seeking_structure)
+Name is Alice (personal_information)
+```
+</CodeGroup>
+
+<Note>Providing more detailed and specific category descriptions will lead to more accurate and relevant memory categorization.</Note>
+
+
+## Default Categories
+Here is the list of **default categories**. If you don't specify any custom categories using the above methods, these will be used as default categories.
+```
+- personal_details
+- family
+- professional_details
+- sports
+- travel
+- food
+- music
+- health
+- technology
+- hobbies
+- fashion
+- entertainment
+- milestones
+- user_preferences
+- misc
+```
+
+<CodeGroup>
+```python Code
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="<your_mem0_api_key>")
+
+messages = [
+    {"role": "user", "content": "Hi, my name is Alice."},
+    {"role": "assistant", "content": "Hi Alice, what sports do you like to play?"},
+    {"role": "user", "content": "I love playing badminton, football, and basketball. I'm quite athletic!"},
+    {"role": "assistant", "content": "That's great! Alice seems to enjoy both individual sports like badminton and team sports like football and basketball."},
+    {"role": "user", "content": "Sometimes, I also draw and sketch in my free time."},
+    {"role": "assistant", "content": "That's cool! I'm sure you're good at it."}
+]
+
+# Add memories with default categories
+client.add(messages, user_id='alice')
+```
+
+```python Memories with categories
+# Following categories will be created for the memories added
+Sometimes draws and sketches in free time (hobbies)
+Is quite athletic (sports)
+Loves playing badminton, football, and basketball (sports)
+Name is Alice (personal_details)
+```
+</CodeGroup>
+
+You can check whether default categories are being used by calling `get_custom_instructions_and_categories()`. If `custom_categories` returns `None`, it means the default categories are being used.
+
+<CodeGroup>
+```python Code
+client.get_custom_instructions_and_categories(["custom_categories"])
+```
+
+```json Output
+{
+    'custom_categories': None
+}
+```
+</CodeGroup>
+
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx" />

docs/features/custom-instructions.mdx

@@ -0,0 +1,74 @@
+---
+title: Custom Instructions
+description: 'Enhance your product experience by adding custom instructions tailored to your needs'
+---
+
+## Introduction to Custom Instructions
+
+Custom instructions allow you to define specific guidelines for your project. This feature helps ensure consistency and provides clear direction for handling project-specific requirements.
+
+Custom instructions are particularly useful when you want to:
+- Define how information should be extracted from conversations
+- Specify what types of data should be captured or ignored
+- Set rules for categorizing and organizing memories
+- Maintain consistent handling of project-specific requirements
+
+When custom instructions are set at the project level, they will be applied to all new memories added within that project. This ensures that your data is processed according to your defined guidelines across your entire project.
+
+## Setting Custom Instructions
+
+You can set custom instructions for your project using the following method:
+
+<CodeGroup>
+```python Code
+# Update custom instructions
+prompt ="""
+Your Task: Extract ONLY health-related information from conversations, focusing on the following areas:
+
+1. Medical Conditions, Symptoms, and Diagnoses:
+   - Illnesses, disorders, or symptoms (e.g., fever, diabetes).
+   - Confirmed or suspected diagnoses.
+
+2. Medications, Treatments, and Procedures:
+   - Prescription or OTC medications (names, dosages).
+   - Treatments, therapies, or medical procedures.
+
+3. Diet, Exercise, and Sleep:
+   - Dietary habits, fitness routines, and sleep patterns.
+
+4. Doctor Visits and Appointments:
+   - Past, upcoming, or regular medical visits.
+
+5. Health Metrics:
+   - Data like weight, BP, cholesterol, or sugar levels.
+
+Guidelines:
+- Focus solely on health-related content.
+- Maintain clarity and context accuracy while recording.
+"""
+response = client.update_project(custom_instructions=prompt)
+print(response)
+```
+
+```json Output
+{
+    "message": "Updated custom instructions"
+}
+```
+</CodeGroup>
+
+You can also retrieve the current custom instructions:
+
+<CodeGroup>
+```python Code
+# Retrieve current custom instructions
+response = client.get_project(fields=["custom_instructions"])
+print(response)
+```
+
+```json Output
+{
+    "custom_instructions": "Your Task: Extract ONLY health-related information from conversations, focusing on the following areas:\n1. Medical Conditions, Symptoms, and Diagnoses - illnesses, disorders, or symptoms (e.g., fever, diabetes), confirmed or suspected diagnoses.\n2. Medications, Treatments, and Procedures - prescription or OTC medications (names, dosages), treatments, therapies, or medical procedures.\n3. Diet, Exercise, and Sleep - dietary habits, fitness routines, and sleep patterns.\n4. Doctor Visits and Appointments - past, upcoming, or regular medical visits.\n5. Health Metrics - data like weight, BP, cholesterol, or sugar levels.\n\nGuidelines: Focus solely on health-related content. Maintain clarity and context accuracy while recording."
+}
+```
+</CodeGroup>

docs/features/custom-prompts.mdx

@@ -0,0 +1,109 @@
+---
+title: Custom Prompts
+description: 'Enhance your product experience by adding custom prompts tailored to your needs'
+---
+
+## Introduction to Custom Prompts
+
+Custom prompts allow you to tailor the behavior of your Mem0 instance to specific use cases or domains. 
+By defining a custom prompt, you can control how information is extracted, processed, and stored in your memory system.
+
+To create an effective custom prompt:
+1. Be specific about the information to extract.
+2. Provide few-shot examples to guide the LLM.
+3. Ensure examples follow the format shown below.
+
+Example of a custom prompt:
+
+```python
+custom_prompt = """
+Please only extract entities containing customer support information, order details, and user information. 
+Here are some few shot examples:
+
+Input: Hi.
+Output: {{"facts" : []}}
+
+Input: The weather is nice today.
+Output: {{"facts" : []}}
+
+Input: My order #12345 hasn't arrived yet.
+Output: {{"facts" : ["Order #12345 not received"]}}
+
+Input: I'm John Doe, and I'd like to return the shoes I bought last week.
+Output: {{"facts" : ["Customer name: John Doe", "Wants to return shoes", "Purchase made last week"]}}
+
+Input: I ordered a red shirt, size medium, but received a blue one instead.
+Output: {{"facts" : ["Ordered red shirt, size medium", "Received blue shirt instead"]}}
+
+Return the facts and customer information in a json format as shown above.
+"""
+
+```
+
+Here we initialize the custom prompt in the config.
+
+```python
+from mem0 import Memory
+
+config = {
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "model": "gpt-4o",
+            "temperature": 0.2,
+            "max_tokens": 1500,
+        }
+    },
+    "custom_prompt": custom_prompt,
+    "version": "v1.1"
+}
+
+m = Memory.from_config(config_dict=config, user_id="alice")
+```
+
+### Example 1
+
+In this example, we are adding a memory of a user ordering a laptop. As seen in the output, the custom prompt is used to extract the relevant information from the user's message.
+
+<CodeGroup>
+```python Code
+m.add("Yesterday, I ordered a laptop, the order id is 12345", user_id="alice")
+```
+
+```json Output
+{
+  "results": [
+    {
+      "memory": "Ordered a laptop",
+      "event": "ADD"
+    },
+    {
+      "memory": "Order ID: 12345",
+      "event": "ADD"
+    },
+    {
+      "memory": "Order placed yesterday",
+      "event": "ADD"
+    }
+  ],
+  "relations": []
+}
+```
+</CodeGroup>
+
+### Example 2
+
+In this example, we are adding a memory of a user liking to go on hikes. This add message is not specific to the use-case mentioned in the custom prompt. 
+Hence, the memory is not added.
+<CodeGroup>
+```python Code
+m.add("I like going to hikes", user_id="alice")
+```
+
+```json Output
+{
+  "results": [],
+  "relations": []
+}
+```
+</CodeGroup>

docs/features/direct-import.mdx

@@ -0,0 +1,102 @@
+---
+title: Direct Import
+description: 'Bypass the memory deduction phase and directly store pre-defined memories for efficient retrieval'
+---
+
+## How to use Direct Import?
+The Direct Import feature allows users to skip the memory deduction phase and directly input pre-defined memories into the system for storage and retrieval.
+To enable this feature, you need to set the `infer` parameter to `False` in the `add` method.
+
+
+<CodeGroup>
+
+
+```python Python
+messages = [
+    {"role": "user", "content": "Alice loves playing badminton"},
+    {"role": "assistant", "content": "That's great! Alice is a fitness freak"},
+    {"role": "user", "content": "Alice mostly cook at home because of gym plan"},
+]
+
+
+client.add(messages, user_id="alice", infer=False)
+```
+
+```markdown Output
+[]
+```
+</CodeGroup>
+
+You can see that the output of add call is an empty list.
+
+<Note> Only messages with the role "user" will be used for storage. Messages with roles such as "assistant" or "system" will be ignored during the storage process. </Note>
+
+
+## How to retrieve memories?
+
+You can retrieve memories using the `search` method.
+
+<CodeGroup>
+
+```python Python
+client.search(query="What is Alice's favorite sport?", user_id="alice", output_format="v1.1")
+```
+
+```json Output
+{
+  "results": [
+    {
+      "id": "19d6d7aa-2454-4e58-96fc-e74d9e9f8dd1",
+      "memory": "Alice loves playing badminton",
+      "user_id": "pc123",
+      "metadata": null,
+      "categories": null,
+      "created_at": "2024-10-15T21:52:11.474901-07:00",
+      "updated_at": "2024-10-15T21:52:11.474912-07:00"
+    }
+  ]
+}
+```
+
+</CodeGroup>
+
+## How to retrieve all memories?
+
+You can retrieve all memories using the `get_all` method.
+
+<CodeGroup>
+
+```python Python
+client.get_all(query="What is Alice's favorite sport?", user_id="alice", output_format="v1.1")
+```
+
+```json Output
+{
+  "results": [
+    {
+      "id": "19d6d7aa-2454-4e58-96fc-e74d9e9f8dd1",
+      "memory": "Alice loves playing badminton",
+      "user_id": "pc123",
+      "metadata": null,
+      "categories": null,
+      "created_at": "2024-10-15T21:52:11.474901-07:00",
+      "updated_at": "2024-10-15T21:52:11.474912-07:00"
+    },
+    {
+      "id": "8557f05d-7b3c-47e5-b409-9886f9e314fc",
+      "memory": "Alice mostly cook at home because of gym plan",
+      "user_id": "pc123",
+      "metadata": null,
+      "categories": null,
+      "created_at": "2024-10-15T21:52:11.474929-07:00",
+      "updated_at": "2024-10-15T21:52:11.474932-07:00"
+    }
+  ]
+}
+```
+
+</CodeGroup>
+
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx" />

docs/features/langchain-tools.mdx

@@ -0,0 +1,328 @@
+---
+title: Langchain Tools
+description: 'Integrate Mem0 with LangChain tools to enable AI agents to store, search, and manage memories through structured interfaces'
+---
+
+## Overview
+
+Mem0 provides a suite of tools for storing, searching, and retrieving memories, enabling agents to maintain context and learn from past interactions. The tools are built as Langchain tools, making them easily integrable with any AI agent implementation.
+
+## Installation
+
+Install the required dependencies:
+
+```bash
+pip install langchain_core
+pip install mem0ai
+```
+
+## Authentication
+
+Import the necessary dependencies and initialize the client:
+
+```python
+from langchain_core.tools import StructuredTool
+from mem0 import MemoryClient
+from pydantic import BaseModel, Field
+from typing import List, Dict, Any, Optional
+
+client = MemoryClient(
+    api_key=your_api_key,
+    org_id=your_org_id,
+    project_id=your_project_id
+)
+```
+
+## Available Tools
+
+Mem0 provides three main tools for memory management:
+
+### 1. ADD Memory Tool
+
+The ADD tool allows you to store new memories with associated metadata. It's particularly useful for saving conversation history and user preferences.
+
+#### Schema
+
+```python
+class Message(BaseModel):
+    role: str = Field(description="Role of the message sender (user or assistant)")
+    content: str = Field(description="Content of the message")
+
+class AddMemoryInput(BaseModel):
+    messages: List[Message] = Field(description="List of messages to add to memory")
+    user_id: str = Field(description="ID of the user associated with these messages")
+    output_format: str = Field(description="Version format for the output")
+    metadata: Optional[Dict[str, Any]] = Field(description="Additional metadata for the messages", default=None)
+
+    class Config:
+        json_schema_extra = {
+            "examples": [{
+                "messages": [
+                    {"role": "user", "content": "Hi, I'm Alex. I'm a vegetarian and I'm allergic to nuts."},
+                    {"role": "assistant", "content": "Hello Alex! I've noted that you're a vegetarian and have a nut allergy."}
+                ],
+                "user_id": "alex",
+                "output_format": "v1.1",
+                "metadata": {"food": "vegan"}
+            }]
+        }
+```
+
+#### Implementation
+
+```python
+def add_memory(messages: List[Message], user_id: str, output_format: str, metadata: Optional[Dict[str, Any]] = None) -> Any:
+    """Add messages to memory with associated user ID and metadata."""
+    message_dicts = [msg.dict() for msg in messages]
+    return client.add(message_dicts, user_id=user_id, output_format=output_format, metadata=metadata)
+
+add_tool = StructuredTool(
+    name="add_memory",
+    description="Add new messages to memory with associated metadata",
+    func=add_memory,
+    args_schema=AddMemoryInput
+)
+```
+
+#### Example Usage
+
+<CodeGroup>
+```python Code
+add_input = {
+    "messages": [
+        {"role": "user", "content": "Hi, I'm Alex. I'm a vegetarian and I'm allergic to nuts."},
+        {"role": "assistant", "content": "Hello Alex! I've noted that you're a vegetarian and have a nut allergy."}
+    ],
+    "user_id": "alex123",
+    "output_format": "v1.1",
+    "metadata": {"food": "vegan"}
+}
+add_result = add_tool.invoke(add_input)
+```
+
+```json Output
+{
+  "results": [
+    {
+      "memory": "Name is Alex",
+      "event": "ADD"
+    },
+    {
+      "memory": "Is a vegetarian", 
+      "event": "ADD"
+    },
+    {
+      "memory": "Is allergic to nuts",
+      "event": "ADD"
+    }
+  ]
+}
+```
+</CodeGroup>
+
+### 2. SEARCH Memory Tool
+
+The SEARCH tool enables querying stored memories using natural language queries and advanced filtering options.
+
+#### Schema
+
+```python
+class SearchMemoryInput(BaseModel):
+    query: str = Field(description="The search query string")
+    filters: Dict[str, Any] = Field(description="Filters to apply to the search")
+    version: str = Field(description="Version of the memory to search")
+
+    class Config:
+        json_schema_extra = {
+            "examples": [{
+                "query": "tell me about my allergies?",
+                "filters": {
+                    "AND": [
+                        {"user_id": "alex"},
+                        {"created_at": {"gte": "2024-01-01", "lte": "2024-12-31"}}
+                    ]
+                },
+                "version": "v2"
+            }]
+        }
+```
+
+#### Implementation
+
+```python
+def search_memory(query: str, filters: Dict[str, Any], version: str) -> Any:
+    """Search memory with the given query and filters."""
+    return client.search(query=query, version=version, filters=filters)
+
+search_tool = StructuredTool(
+    name="search_memory",
+    description="Search through memories with a query and filters",
+    func=search_memory,
+    args_schema=SearchMemoryInput
+)
+```
+
+#### Example Usage
+
+<CodeGroup>
+```python Code
+search_input = {
+    "query": "what is my name?",
+    "filters": {
+        "AND": [
+            {"created_at": {"gte": "2024-07-20", "lte": "2024-12-10"}},
+            {"user_id": "alex123"}
+        ]
+    },
+    "version": "v2"
+}
+result = search_tool.invoke(search_input)
+```
+
+```json Output
+[
+  {
+    "id": "1a75e827-7eca-45ea-8c5c-cfd43299f061",
+    "memory": "Name is Alex",
+    "user_id": "alex123", 
+    "hash": "d0fccc8fa47f7a149ee95750c37bb0ca",
+    "metadata": {
+      "food": "vegan"
+    },
+    "categories": [
+      "personal_details"
+    ],
+    "created_at": "2024-11-27T16:53:43.276872-08:00",
+    "updated_at": "2024-11-27T16:53:43.276885-08:00",
+    "score": 0.3810526501504994
+  }
+]
+```
+</CodeGroup>
+
+### 3. GET_ALL Memory Tool
+
+The GET_ALL tool retrieves all memories matching specified criteria, with support for pagination.
+
+#### Schema
+
+```python
+class GetAllMemoryInput(BaseModel):
+    version: str = Field(description="Version of the memory to retrieve")
+    filters: Dict[str, Any] = Field(description="Filters to apply to the retrieval")
+    page: Optional[int] = Field(description="Page number for pagination", default=1)
+    page_size: Optional[int] = Field(description="Number of items per page", default=50)
+
+    class Config:
+        json_schema_extra = {
+            "examples": [{
+                "version": "v2",
+                "filters": {
+                    "AND": [
+                        {"user_id": "alex"},
+                        {"created_at": {"gte": "2024-07-01", "lte": "2024-07-31"}},
+                        {"categories": {"contains": "food_preferences"}}
+                    ]
+                },
+                "page": 1,
+                "page_size": 50
+            }]
+        }
+```
+
+#### Implementation
+
+```python
+def get_all_memory(version: str, filters: Dict[str, Any], page: int = 1, page_size: int = 50) -> Any:
+    """Retrieve all memories matching the specified criteria."""
+    return client.get_all(version=version, filters=filters, page=page, page_size=page_size)
+
+get_all_tool = StructuredTool(
+    name="get_all_memory",
+    description="Retrieve all memories matching specified filters",
+    func=get_all_memory,
+    args_schema=GetAllMemoryInput
+)
+```
+
+#### Example Usage
+
+<CodeGroup>
+```python Code
+get_all_input = {
+    "version": "v2",
+    "filters": {
+        "AND": [
+            {"user_id": "alex123"},
+            {"created_at": {"gte": "2024-07-01", "lte": "2024-12-31"}}
+        ]
+    },
+    "page": 1,
+    "page_size": 50
+}
+get_all_result = get_all_tool.invoke(get_all_input)
+```
+
+```json Output
+{
+  "count": 3,
+  "next": null,
+  "previous": null,
+  "results": [
+    {
+      "id": "1a75e827-7eca-45ea-8c5c-cfd43299f061",
+      "memory": "Name is Alex",
+      "user_id": "alex123", 
+      "hash": "d0fccc8fa47f7a149ee95750c37bb0ca",
+      "metadata": {
+        "food": "vegan"
+      },
+      "categories": [
+        "personal_details"
+      ],
+      "created_at": "2024-11-27T16:53:43.276872-08:00",
+      "updated_at": "2024-11-27T16:53:43.276885-08:00"
+    },
+    {
+      "id": "91509588-0b39-408a-8df3-84b3bce8c521",
+      "memory": "Is a vegetarian",
+      "user_id": "alex123",
+      "hash": "ce6b1c84586772ab9995a9477032df99", 
+      "metadata": {
+        "food": "vegan"
+      },
+      "categories": [
+        "user_preferences",
+        "food"
+      ],
+      "created_at": "2024-11-27T16:53:43.308027-08:00",
+      "updated_at": "2024-11-27T16:53:43.308037-08:00"
+    },
+    {
+      "id": "8d74f7a0-6107-4589-bd6f-210f6bf4fbbb",
+      "memory": "Is allergic to nuts",
+      "user_id": "alex123",
+      "hash": "7873cd0e5a29c513253d9fad038e758b",
+      "metadata": {
+        "food": "vegan"
+      },
+      "categories": [
+        "health"
+      ],
+      "created_at": "2024-11-27T16:53:43.337253-08:00",
+      "updated_at": "2024-11-27T16:53:43.337262-08:00"
+    }
+  ]
+}
+```
+</CodeGroup>
+
+## Integration with AI Agents
+
+All tools are implemented as Langchain `StructuredTool` instances, making them compatible with any AI agent that supports the Langchain tools interface. To use these tools with your agent:
+
+1. Initialize the tools as shown above
+2. Add the tools to your agent's toolset
+3. The agent can now use these tools to manage memories through natural language interactions
+
+Each tool provides structured input validation through Pydantic models and returns consistent responses that can be processed by your agent.

docs/features/memory-export.mdx

@@ -0,0 +1,145 @@
+---
+title: Memory Export
+description: 'Export memories in a structured format using customizable Pydantic schemas'
+---
+
+## Overview
+
+The Memory Export feature allows you to create structured exports of memories using customizable Pydantic schemas. This process enables you to transform your stored memories into specific data formats that match your needs. You can apply various filters to narrow down which memories to export and define exactly how the data should be structured.
+
+## Creating a Memory Export
+
+To create a memory export, you'll need to:
+1. Define your schema structure
+2. Submit an export job
+3. Retrieve the exported data
+
+### Define Schema
+
+Here's an example schema for extracting professional profile information:
+
+```json
+{
+    "$defs": {
+        "EducationLevel": {
+            "enum": ["high_school", "bachelors", "masters"],
+            "title": "EducationLevel",
+            "type": "string"
+        },
+        "EmploymentStatus": {
+            "enum": ["full_time", "part_time", "student"],
+            "title": "EmploymentStatus", 
+            "type": "string"
+        }
+    },
+    "properties": {
+        "full_name": {
+            "anyOf": [
+                {
+                    "maxLength": 100,
+                    "minLength": 2,
+                    "type": "string"
+                },
+                {
+                    "type": "null"
+                }
+            ],
+            "default": null,
+            "description": "The professional's full name",
+            "title": "Full Name"
+        },
+        "current_role": {
+            "anyOf": [
+                {
+                    "type": "string"
+                },
+                {
+                    "type": "null"
+                }
+            ],
+            "default": null,
+            "description": "Current job title or role",
+            "title": "Current Role"
+        }
+    },
+    "title": "ProfessionalProfile",
+    "type": "object"
+}
+```
+
+### Submit Export Job
+
+<CodeGroup>
+
+```python Python
+response = client.create_memory_export(
+    schema=json_schema,
+    user_id="user123"
+)
+print(response)
+```
+
+```bash cURL
+curl -X POST "https://api.mem0.ai/v1/memories/export/" \
+     -H "Authorization: Token your-api-key" \
+     -H "Content-Type: application/json" \
+     -d '{
+         "schema": {json_schema},
+         "user_id": "user123"
+     }'
+```
+
+```json Output
+{
+    "message": "Memory export request received. The export will be ready in a few seconds.",
+    "id": "550e8400-e29b-41d4-a716-446655440000"
+}
+```
+
+</CodeGroup>
+
+### Retrieve Export
+
+Once the export job is complete, you can retrieve the structured data:
+
+<CodeGroup>
+
+```python Python
+response = client.get_memory_export(user_id="user123")
+print(response)
+```
+
+```bash cURL
+curl -X GET "https://api.mem0.ai/v1/memories/export/?user_id=user123" \
+     -H "Authorization: Token your-api-key"
+```
+
+```json Output
+{
+    "full_name": "John Doe",
+    "current_role": "Senior Software Engineer",
+    "years_experience": 8,
+    "employment_status": "full_time",
+    "education_level": "masters",
+    "skills": ["Python", "AWS", "Machine Learning"]
+}
+```
+
+</CodeGroup>
+
+## Available Filters
+
+You can apply various filters to customize which memories are included in the export:
+
+- `user_id`: Filter memories by specific user
+- `agent_id`: Filter memories by specific agent
+- `run_id`: Filter memories by specific run
+- `session_id`: Filter memories by specific session
+
+<Note>
+The export process may take some time to complete, especially when dealing with a large number of memories or complex schemas.
+</Note>
+
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx" />